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TypeAgent/python/fineTuning/unsloth

python/fineTuning/unsloth/extraction.txt

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Add grammar generation infrastructure and NFA cache integration (#1870)c9765f9

4 months ago

1	`================================================================================`
2	`Message 1:`
3	`MARIO LLERENA: (Through interpreter) A thief doesn't announce he's coming. We've announced we're coming. Trump knows. Or has he not been notified?`
4	`--------------------------------------------------------------------------------`
5	`RAKE Keywords:`
6	`- trump knows (score: 4.00) → trump know`
7	`- mario llerena (score: 4.00) → MARIO LLERENA`
8	`- thief (score: 1.00)`
9	`- notified (score: 1.00) → notify`
10	`- interpreter (score: 1.00)`
11	`- coming (score: 1.00) → come`
12	`- coming (score: 1.00) → come`
13	`- announced (score: 1.00) → announce`
14	`- announce (score: 1.00)`
15	`Total keywords: 9 extracted in 0.0214 seconds`
16
17	`YAKE Keywords:`
18	`- MARIO LLERENA (score: 0.01) → MARIO LLERENA`
19	`- MARIO (score: 0.08) → MARIO`
20	`- LLERENA (score: 0.08) → LLERENA`
21	`- coming (score: 0.16) → come`
22	`- interpreter (score: 0.18)`
23	`- announce he coming (score: 0.21)`
24	`- thief (score: 0.28)`
25	`- announce (score: 0.28)`
26	`- announced we coming (score: 0.42)`
27	`- Trump (score: 0.53)`
28	`- announced (score: 0.58) → announce`
29	`- notified (score: 0.58) → notify`
30	`Total keywords: 12 extracted in 0.0000 seconds`
31
32	`KeyBERT Keywords:`
33	`- trump knows notified (score: 0.65)`
34	`- thief doesn announce (score: 0.61)`
35	`- announced coming trump (score: 0.58)`
36	`- doesn announce coming (score: 0.55)`
37	`- coming trump knows (score: 0.53)`
38	`- announce coming (score: 0.51)`
39	`- announce coming ve (score: 0.50)`
40	`- ve announced coming (score: 0.48)`
41	`- coming ve announced (score: 0.47)`
42	`- doesn announce (score: 0.47)`
43	`- knows notified (score: 0.46)`
44	`- trump knows (score: 0.43)`
45	`- llerena interpreter thief (score: 0.43)`
46	`- announce (score: 0.43)`
47	`- mario llerena interpreter (score: 0.41)`
48	`- announced coming (score: 0.41)`
49	`- ve announced (score: 0.40)`
50	`- mario llerena (score: 0.40)`
51	`- coming trump (score: 0.39)`
52	`- notified (score: 0.39)`
53	`Total keywords: 20 extracted in 0.0769 seconds`
54
55	`Dependency Relations (extracted in 0.0100sec):`
56
57	`Sentence 1: MARIO LLERENA: (Through interpreter)`
58	`MARIO (PROPN) --[compound]--> LLERENA (PROPN)`
59	`LLERENA (PROPN) --[ROOT]--> LLERENA (PROPN)`
60	`: (PUNCT) --[punct]--> LLERENA (PROPN)`
61	`( (PUNCT) --[punct]--> LLERENA (PROPN)`
62	`Through (ADP) --[prep]--> LLERENA (PROPN)`
63	`interpreter (NOUN) --[pobj]--> Through (ADP)`
64	`) (PUNCT) --[punct]--> LLERENA (PROPN)`
65
66	`Sentence 2: A thief doesn't announce he's coming.`
67	`A (DET) --[det]--> thief (NOUN)`
68	`thief (NOUN) --[nsubj]--> announce (VERB)`
69	`does (AUX) --[aux]--> announce (VERB)`
70	`n't (PART) --[neg]--> announce (VERB)`
71	`announce (VERB) --[ROOT]--> announce (VERB)`
72	`he (PRON) --[nsubj]--> coming (VERB)`
73	`'s (AUX) --[aux]--> coming (VERB)`
74	`coming (VERB) --[ccomp]--> announce (VERB)`
75	`. (PUNCT) --[punct]--> announce (VERB)`
76
77	`Sentence 3: We've announced we're coming.`
78	`We (PRON) --[nsubj]--> announced (VERB)`
79	`'ve (AUX) --[aux]--> announced (VERB)`
80	`announced (VERB) --[ROOT]--> announced (VERB)`
81	`we (PRON) --[nsubj]--> coming (VERB)`
82	`'re (AUX) --[aux]--> coming (VERB)`
83	`coming (VERB) --[ccomp]--> announced (VERB)`
84	`. (PUNCT) --[punct]--> announced (VERB)`
85
86	`Sentence 4: Trump knows.`
87	`Trump (NOUN) --[nsubj]--> knows (VERB)`
88	`knows (VERB) --[ROOT]--> knows (VERB)`
89	`. (PUNCT) --[punct]--> knows (VERB)`
90
91	`Sentence 5: Or has he not been notified?`
92	`Or (CCONJ) --[cc]--> notified (VERB)`
93	`has (AUX) --[aux]--> notified (VERB)`
94	`he (PRON) --[nsubjpass]--> notified (VERB)`
95	`not (PART) --[neg]--> notified (VERB)`
96	`been (AUX) --[auxpass]--> notified (VERB)`
97	`notified (VERB) --[ROOT]--> notified (VERB)`
98	`? (PUNCT) --[punct]--> notified (VERB)`
99
100	`Total sentences: 5`
101
102	`RAKE Keyphrase Relationships:`
103	`verb phrase: "coming 's" contains [coming], [coming]`
104	`verb phrase: "announced 've" contains [announced], [announce]`
105	`verb phrase: "coming 're" contains [coming], [coming]`
106	`Total relationships found: 3`
107
108	`YAKE Keyphrase Relationships:`
109	`noun phrase: "MARIO LLERENA" contains [mario llerena], [mario], [llerena]`
110	`verb phrase: "announced 've" contains [announce], [announced]`
111	`Total relationships found: 2`
112
113	`Timing Comparison:`
114	`RAKE: 0.0214sec`
115	`YAKE: 0.0000sec`
116	`KeyBERT: 0.0769sec`
117	`Dependencies: 0.0100sec`
118	`Fastest: YAKE`
119
120	`================================================================================`
121	`Message 2:`
122	`DAVE MISTICH: Joe Manchin has been a fixture in West Virginia politics for decades. And with a 50-50 Senate, he's also become a fixture in debates over Democrats' agenda, including Biden's spending plan. The conservative Democrat has demanded the proposal shrink in size. That's meant entire provisions have been left out, including free community college and paid family leave. His position as a negotiator hasn't set well with some Democrats back home.`
123	`--------------------------------------------------------------------------------`
124	`RAKE Keywords:`
125	`- west virginia politics (score: 9.00) → West Virginia politic`
126	`- paid family leave (score: 9.00) → pay family leave`
127	`- meant entire provisions (score: 9.00) → mean entire provision`
128	`- democrats back home (score: 8.00) → Democrats back home`
129	`- spending plan (score: 4.00)`
130	`- set well (score: 4.00)`
131	`- proposal shrink (score: 4.00)`
132	`- joe manchin (score: 4.00) → Joe Manchin`
133	`- including biden (score: 4.00) → include Biden`
134	`- dave mistich (score: 4.00) → DAVE MISTICH`
135	`- conservative democrat (score: 4.00) → conservative Democrat`
136	`- also become (score: 4.00)`
137	`- 50 senate (score: 3.50) → 50 Senate`
138	`- democrats (score: 2.00) → Democrats`
139	`- 50 (score: 1.50)`
140	`- size (score: 1.00)`
141	`- position (score: 1.00)`
142	`- negotiator (score: 1.00)`
143	`- left (score: 1.00) → leave`
144	`- fixture (score: 1.00)`
145	`- fixture (score: 1.00)`
146	`- demanded (score: 1.00) → demand`
147	`- decades (score: 1.00) → decade`
148	`- debates (score: 1.00) → debate`
149	`- agenda (score: 1.00)`
150	`Total keywords: 25 extracted in 0.0000 seconds`
151
152	`YAKE Keywords:`
153	`- West Virginia politics (score: 0.00) → West Virginia politic`
154	`- DAVE MISTICH (score: 0.00) → DAVE MISTICH`
155	`- Joe Manchin (score: 0.01) → Joe Manchin`
156	`- West Virginia (score: 0.01) → West Virginia`
157	`- Virginia politics (score: 0.01) → Virginia politic`
158	`- fixture in West (score: 0.02) → fixture in West`
159	`- politics for decades (score: 0.03) → politic for decade`
160	`- Biden spending plan (score: 0.05)`
161	`- including Biden spending (score: 0.05)`
162	`- DAVE (score: 0.06) → DAVE`
163	`- MISTICH (score: 0.06) → MISTICH`
164	`- Joe (score: 0.06) → Joe`
165	`- Manchin (score: 0.08) → Manchin`
166	`- West (score: 0.08) → West`
167	`- Virginia (score: 0.08) → Virginia`
168	`- Democrats' agenda (score: 0.09)`
169	`- 50-50 Senate (score: 0.09)`
170	`- including Biden (score: 0.09) → include Biden`
171	`- fixture in debates (score: 0.10) → fixture in debate`
172	`- fixture (score: 0.11)`
173	`Total keywords: 20 extracted in 0.0299 seconds`
174
175	`KeyBERT Keywords:`
176	`- mistich joe manchin (score: 0.54)`
177	`- joe manchin (score: 0.53)`
178	`- virginia politics decades (score: 0.49)`
179	`- virginia politics (score: 0.46)`
180	`- west virginia politics (score: 0.46)`
181	`- democrat demanded proposal (score: 0.45)`
182	`- dave mistich joe (score: 0.45)`
183	`- joe manchin fixture (score: 0.44)`
184	`- manchin (score: 0.44)`
185	`- biden spending plan (score: 0.43)`
186	`- mistich joe (score: 0.42)`
187	`- conservative democrat demanded (score: 0.41)`
188	`- plan conservative democrat (score: 0.40)`
189	`- biden spending (score: 0.40)`
190	`- spending plan conservative (score: 0.39)`
191	`- debates democrats (score: 0.39)`
192	`- dave mistich (score: 0.39)`
193	`- demanded proposal shrink (score: 0.39)`
194	`- agenda including biden (score: 0.38)`
195	`- including biden (score: 0.38)`
196	`Total keywords: 20 extracted in 0.0961 seconds`
197
198	`Dependency Relations (extracted in 0.0186sec):`
199
200	`Sentence 1: DAVE MISTICH: Joe Manchin has been a fixture in West Virginia politics for decades.`
201	`DAVE (PROPN) --[compound]--> MISTICH (PROPN)`
202	`MISTICH (PROPN) --[ROOT]--> MISTICH (PROPN)`
203	`: (PUNCT) --[punct]--> MISTICH (PROPN)`
204	`Joe (PROPN) --[compound]--> Manchin (PROPN)`
205	`Manchin (PROPN) --[nsubj]--> been (AUX)`
206	`has (AUX) --[aux]--> been (AUX)`
207	`been (AUX) --[acl]--> MISTICH (PROPN)`
208	`a (DET) --[det]--> fixture (NOUN)`
209	`fixture (NOUN) --[attr]--> been (AUX)`
210	`in (ADP) --[prep]--> fixture (NOUN)`
211	`West (PROPN) --[compound]--> Virginia (PROPN)`
212	`Virginia (PROPN) --[compound]--> politics (NOUN)`
213	`politics (NOUN) --[pobj]--> in (ADP)`
214	`for (ADP) --[prep]--> been (AUX)`
215	`decades (NOUN) --[pobj]--> for (ADP)`
216	`. (PUNCT) --[punct]--> been (AUX)`
217
218	`Sentence 2: And with a 50-50 Senate, he's also become a fixture in debates over Democrats' agenda, including Biden's spending plan.`
219	`And (CCONJ) --[cc]--> become (VERB)`
220	`with (ADP) --[prep]--> become (VERB)`
221	`a (DET) --[det]--> Senate (PROPN)`
222	`50 (NUM) --[nummod]--> Senate (PROPN)`
223	`- (SYM) --[punct]--> 50 (NUM)`
224	`50 (NUM) --[prep]--> 50 (NUM)`
225	`Senate (PROPN) --[pobj]--> with (ADP)`
226	`, (PUNCT) --[punct]--> become (VERB)`
227	`he (PRON) --[nsubjpass]--> become (VERB)`
228	`'s (AUX) --[auxpass]--> become (VERB)`
229	`also (ADV) --[advmod]--> become (VERB)`
230	`become (VERB) --[ROOT]--> become (VERB)`
231	`a (DET) --[det]--> fixture (NOUN)`
232	`fixture (NOUN) --[attr]--> become (VERB)`
233	`in (ADP) --[prep]--> fixture (NOUN)`
234	`debates (NOUN) --[pobj]--> in (ADP)`
235	`over (ADP) --[prep]--> debates (NOUN)`
236	`Democrats (PROPN) --[poss]--> agenda (NOUN)`
237	`' (PART) --[case]--> Democrats (PROPN)`
238	`agenda (NOUN) --[pobj]--> over (ADP)`
239	`, (PUNCT) --[punct]--> fixture (NOUN)`
240	`including (VERB) --[prep]--> fixture (NOUN)`
241	`Biden (PROPN) --[poss]--> plan (NOUN)`
242	`'s (PART) --[case]--> Biden (PROPN)`
243	`spending (NOUN) --[compound]--> plan (NOUN)`
244	`plan (NOUN) --[pobj]--> including (VERB)`
245	`. (PUNCT) --[punct]--> become (VERB)`
246
247	`Sentence 3: The conservative Democrat has demanded the proposal shrink in size.`
248	`The (DET) --[det]--> Democrat (PROPN)`
249	`conservative (ADJ) --[amod]--> Democrat (PROPN)`
250	`Democrat (PROPN) --[nsubj]--> demanded (VERB)`
251	`has (AUX) --[aux]--> demanded (VERB)`
252	`demanded (VERB) --[ROOT]--> demanded (VERB)`
253	`the (DET) --[det]--> proposal (NOUN)`
254	`proposal (NOUN) --[nsubj]--> shrink (NOUN)`
255	`shrink (NOUN) --[ccomp]--> demanded (VERB)`
256	`in (ADP) --[prep]--> shrink (NOUN)`
257	`size (NOUN) --[pobj]--> in (ADP)`
258	`. (PUNCT) --[punct]--> demanded (VERB)`
259
260	`Sentence 4: That's meant entire provisions have been left out, including free community college and paid family leave.`
261	`That (PRON) --[nsubjpass]--> meant (VERB)`
262	`'s (AUX) --[auxpass]--> meant (VERB)`
263	`meant (VERB) --[ROOT]--> meant (VERB)`
264	`entire (ADJ) --[amod]--> provisions (NOUN)`
265	`provisions (NOUN) --[nsubjpass]--> left (VERB)`
266	`have (AUX) --[aux]--> left (VERB)`
267	`been (AUX) --[auxpass]--> left (VERB)`
268	`left (VERB) --[ccomp]--> meant (VERB)`
269	`out (ADP) --[prt]--> left (VERB)`
270	`, (PUNCT) --[punct]--> left (VERB)`
271	`including (VERB) --[prep]--> left (VERB)`
272	`free (ADJ) --[amod]--> community (NOUN)`
273	`community (NOUN) --[compound]--> college (NOUN)`
274	`college (NOUN) --[pobj]--> including (VERB)`
275	`and (CCONJ) --[cc]--> left (VERB)`
276	`paid (VERB) --[conj]--> left (VERB)`
277	`family (NOUN) --[dobj]--> paid (VERB)`
278	`leave (VERB) --[ccomp]--> meant (VERB)`
279	`. (PUNCT) --[punct]--> meant (VERB)`
280
281	`Sentence 5: His position as a negotiator hasn't set well with some Democrats back home.`
282	`His (PRON) --[poss]--> position (NOUN)`
283	`position (NOUN) --[nsubj]--> set (VERB)`
284	`as (ADP) --[prep]--> position (NOUN)`
285	`a (DET) --[det]--> negotiator (NOUN)`
286	`negotiator (NOUN) --[pobj]--> as (ADP)`
287	`has (AUX) --[aux]--> set (VERB)`
288	`n't (PART) --[neg]--> set (VERB)`
289	`set (VERB) --[ROOT]--> set (VERB)`
290	`well (ADV) --[advmod]--> set (VERB)`
291	`with (ADP) --[prep]--> set (VERB)`
292	`some (DET) --[det]--> Democrats (PROPN)`
293	`Democrats (PROPN) --[pobj]--> with (ADP)`
294	`back (ADV) --[advmod]--> home (ADV)`
295	`home (ADV) --[advmod]--> set (VERB)`
296	`. (PUNCT) --[punct]--> set (VERB)`
297
298	`Total sentences: 5`
299
300	`RAKE Keyphrase Relationships:`
301	`noun phrase: "a fixture" contains [fixture], [fixture]`
302	`noun phrase: "a 50-50 Senate" contains [50 senate], [50]`
303	`noun phrase: "a fixture" contains [fixture], [fixture]`
304	`noun phrase: "Democrats' agenda" contains [democrats], [agenda]`
305	`verb phrase: "become with 's also fixture" contains [fixture], [fixture]`
306	`Total relationships found: 5`
307
308	`YAKE Keyphrase Relationships:`
309	`noun phrase: "DAVE MISTICH" contains [dave mistich], [dave], [mistich]`
310	`noun phrase: "Joe Manchin" contains [joe manchin], [joe], [manchin]`
311	`noun phrase: "West Virginia politics" contains [west virginia politics], [west virginia], [virginia politics], [west], [virginia]`
312	`Total relationships found: 3`
313
314	`Timing Comparison:`
315	`RAKE: 0.0000sec`
316	`YAKE: 0.0299sec`
317	`KeyBERT: 0.0961sec`
318	`Dependencies: 0.0186sec`
319	`Fastest: RAKE`
320
321	`================================================================================`
322	`Message 3:`
323	HORSLEY: That's right. China is still subject to these tariffs. China is not a big supplier of steel and aluminum to the United States, but it is thought to be really responsible for the global glut in those products that's driven down prices. Russia is still subject to the tariffs, and Japan is still subject.Now, Japan is a relatively significant supplier of steel to the U.S., and of course it's an American ally. But Japan was never given one of those temporary waivers like those other countries, so customers of Japanese steel and aluminum have been seeing a tariff - 25 percent on steel, 10 percent on aluminum - since late March. That was a source of some friction when Japanese Prime Minister Shinzo Abe was down in Florida meeting with President Trump two weeks ago.Also, South Korea has been given a permanent exemption from the steel tariffs as part of a renegotiated free trade agreement. In exchange, though, South Korea had to agree to cut its steel exports to the U.S. by about 30 percent. And the White House says it's looking to impose similar quotas on some of these other countries.
324	`--------------------------------------------------------------------------------`
325	`RAKE Keywords:`
326	`- white house says (score: 9.00) → White House say`
327	`- temporary waivers like (score: 9.00) → temporary waiver like`
328	`- since late march (score: 9.00) → since late March`
329	`- impose similar quotas (score: 9.00) → impose similar quota`
330	`- relatively significant supplier (score: 8.50)`
331	`- never given one (score: 8.00) → never give one`
332	`- big supplier (score: 4.50)`
333	`- united states (score: 4.00) → United States`
334	`- still subject (score: 4.00)`
335	`- still subject (score: 4.00)`
336	`- still subject (score: 4.00)`
337	`- south korea (score: 4.00) → South Korea`
338	`- south korea (score: 4.00) → South Korea`
339	`- really responsible (score: 4.00)`
340	`- permanent exemption (score: 4.00)`
341	`- global glut (score: 4.00)`
342	`- florida meeting (score: 4.00) → Florida meeting`
343	`- american ally (score: 4.00)`
344	`- 30 percent (score: 4.00)`
345	`- 25 percent (score: 4.00)`
346	`- 10 percent (score: 4.00)`
347	`- steel exports (score: 3.50) → steel export`
348	`- japanese steel (score: 3.50)`
349	`- steel tariffs (score: 2.83) → steel tariff`
350	`- given (score: 2.00) → give`
351	`- steel (score: 1.50)`
352	`- steel (score: 1.50)`
353	`- steel (score: 1.50)`
354	`- tariffs (score: 1.33) → tariff`
355	`- tariffs (score: 1.33) → tariff`
356	`- u (score: 1.00)`
357	`- u (score: 1.00)`
358	`- thought (score: 1.00) → think`
359	`- though (score: 1.00)`
360	`- tariff (score: 1.00)`
361	`- source (score: 1.00)`
362	`- seeing (score: 1.00) → see`
363	`- russia (score: 1.00) → Russia`
364	`- right (score: 1.00)`
365	`- products (score: 1.00) → product`
366	`- prices (score: 1.00) → price`
367	`- part (score: 1.00)`
368	`- looking (score: 1.00) → look`
369	`- japan (score: 1.00) → Japan`
370	`- japan (score: 1.00) → Japan`
371	`- japan (score: 1.00) → Japan`
372	`- horsley (score: 1.00) → HORSLEY`
373	`- friction (score: 1.00)`
374	`- exchange (score: 1.00)`
375	`- driven (score: 1.00) → drive`
376	`- cut (score: 1.00)`
377	`- customers (score: 1.00) → customer`
378	`- course (score: 1.00)`
379	`- countries (score: 1.00) → country`
380	`- countries (score: 1.00) → country`
381	`- china (score: 1.00) → China`
382	`- china (score: 1.00) → China`
383	`- aluminum (score: 1.00)`
384	`- aluminum (score: 1.00)`
385	`- aluminum (score: 1.00)`
386	`- also (score: 1.00)`
387	`- agree (score: 1.00)`
388	`- ., (score: 1.00)`
389	`Total keywords: 63 extracted in 0.0000 seconds`
390
391	`YAKE Keywords:`
392	`- South Korea (score: 0.05) → South Korea`
393	`- HORSLEY (score: 0.06) → HORSLEY`
394	`- Japanese Prime Minister (score: 0.07) → japanese Prime Minister`
395	`- steel (score: 0.08)`
396	`- United States (score: 0.08) → United States`
397	`- Prime Minister Shinzo (score: 0.08) → Prime Minister Shinzo`
398	`- Minister Shinzo Abe (score: 0.08) → Minister Shinzo Abe`
399	`- Japan (score: 0.09) → Japan`
400	`- percent (score: 0.13)`
401	`- South (score: 0.14) → South`
402	`- Japanese Prime (score: 0.15) → japanese Prime`
403	`- China (score: 0.15) → China`
404	`- aluminum (score: 0.16)`
405	`- tariffs (score: 0.16) → tariff`
406	`- Korea (score: 0.17) → Korea`
407	`- Prime Minister (score: 0.17) → Prime Minister`
408	`- Minister Shinzo (score: 0.17) → Minister Shinzo`
409	`- Shinzo Abe (score: 0.17) → Shinzo Abe`
410	`- President Trump (score: 0.17) → President Trump`
411	`- Japanese (score: 0.17)`
412	`Total keywords: 20 extracted in 0.0254 seconds`
413
414	`KeyBERT Keywords:`
415	`- steel tariffs (score: 0.69)`
416	`- tariffs japan subject (score: 0.67)`
417	`- subject tariffs japan (score: 0.67)`
418	`- tariffs japan (score: 0.66)`
419	`- china subject tariffs (score: 0.66)`
420	`- tariffs china (score: 0.65)`
421	`- exemption steel tariffs (score: 0.64)`
422	`- subject tariffs china (score: 0.63)`
423	`- steel tariffs renegotiated (score: 0.61)`
424	`- tariffs china big (score: 0.58)`
425	`- tariffs (score: 0.58)`
426	`- russia subject tariffs (score: 0.56)`
427	`- aluminum seeing tariff (score: 0.55)`
428	`- subject tariffs (score: 0.54)`
429	`- tariff (score: 0.53)`
430	`- steel exports (score: 0.52)`
431	`- seeing tariff (score: 0.51)`
432	`- japanese steel aluminum (score: 0.49)`
433	`- tariffs renegotiated (score: 0.49)`
434	`- tariff 25 percent (score: 0.49)`
435	`Total keywords: 20 extracted in 0.1860 seconds`
436
437	`Dependency Relations (extracted in 0.0253sec):`
438
439	`Sentence 1: HORSLEY:`
440	`HORSLEY (PROPN) --[ROOT]--> HORSLEY (PROPN)`
441	`: (PUNCT) --[punct]--> HORSLEY (PROPN)`
442
443	`Sentence 2: That's right.`
444	`That (PRON) --[nsubj]--> 's (AUX)`
445	`'s (AUX) --[ROOT]--> 's (AUX)`
446	`right (ADJ) --[acomp]--> 's (AUX)`
447	`. (PUNCT) --[punct]--> 's (AUX)`
448
449	`Sentence 3: China is still subject to these tariffs.`
450	`China (PROPN) --[nsubj]--> is (AUX)`
451	`is (AUX) --[ROOT]--> is (AUX)`
452	`still (ADV) --[advmod]--> is (AUX)`
453	`subject (ADJ) --[acomp]--> is (AUX)`
454	`to (ADP) --[prep]--> subject (ADJ)`
455	`these (DET) --[det]--> tariffs (NOUN)`
456	`tariffs (NOUN) --[pobj]--> to (ADP)`
457	`. (PUNCT) --[punct]--> is (AUX)`
458
459	`Sentence 4: China is not a big supplier of steel and aluminum to the United States, but it is thought to be really responsible for the global glut in those products that's driven down prices.`
460	`China (PROPN) --[nsubj]--> is (AUX)`
461	`is (AUX) --[ROOT]--> is (AUX)`
462	`not (PART) --[neg]--> is (AUX)`
463	`a (DET) --[det]--> supplier (NOUN)`
464	`big (ADJ) --[amod]--> supplier (NOUN)`
465	`supplier (NOUN) --[attr]--> is (AUX)`
466	`of (ADP) --[prep]--> supplier (NOUN)`
467	`steel (NOUN) --[pobj]--> of (ADP)`
468	`and (CCONJ) --[cc]--> steel (NOUN)`
469	`aluminum (NOUN) --[conj]--> steel (NOUN)`
470	`to (ADP) --[prep]--> supplier (NOUN)`
471	`the (DET) --[det]--> States (PROPN)`
472	`United (PROPN) --[compound]--> States (PROPN)`
473	`States (PROPN) --[pobj]--> to (ADP)`
474	`, (PUNCT) --[punct]--> is (AUX)`
475	`but (CCONJ) --[cc]--> is (AUX)`
476	`it (PRON) --[nsubjpass]--> thought (VERB)`
477	`is (AUX) --[auxpass]--> thought (VERB)`
478	`thought (VERB) --[conj]--> is (AUX)`
479	`to (PART) --[aux]--> be (AUX)`
480	`be (AUX) --[xcomp]--> thought (VERB)`
481	`really (ADV) --[advmod]--> responsible (ADJ)`
482	`responsible (ADJ) --[acomp]--> be (AUX)`
483	`for (ADP) --[prep]--> responsible (ADJ)`
484	`the (DET) --[det]--> glut (NOUN)`
485	`global (ADJ) --[amod]--> glut (NOUN)`
486	`glut (NOUN) --[pobj]--> for (ADP)`
487	`in (ADP) --[prep]--> glut (NOUN)`
488	`those (DET) --[det]--> products (NOUN)`
489	`products (NOUN) --[pobj]--> in (ADP)`
490	`that (PRON) --[nsubjpass]--> driven (VERB)`
491	`'s (AUX) --[auxpass]--> driven (VERB)`
492	`driven (VERB) --[relcl]--> products (NOUN)`
493	`down (ADP) --[prt]--> driven (VERB)`
494	`prices (NOUN) --[dobj]--> driven (VERB)`
495	`. (PUNCT) --[punct]--> thought (VERB)`
496
497	`Sentence 5: Russia is still subject to the tariffs, and Japan is still subject.`
498	`Russia (PROPN) --[nsubj]--> is (AUX)`
499	`is (AUX) --[ROOT]--> is (AUX)`
500	`still (ADV) --[advmod]--> is (AUX)`
501	`subject (ADJ) --[acomp]--> is (AUX)`
502	`to (ADP) --[prep]--> subject (ADJ)`
503	`the (DET) --[det]--> tariffs (NOUN)`
504	`tariffs (NOUN) --[pobj]--> to (ADP)`
505	`, (PUNCT) --[punct]--> is (AUX)`
506	`and (CCONJ) --[cc]--> is (AUX)`
507	`Japan (PROPN) --[nsubj]--> is (AUX)`
508	`is (AUX) --[conj]--> is (AUX)`
509	`still (ADV) --[advmod]--> is (AUX)`
510	`subject (ADJ) --[acomp]--> is (AUX)`
511	`. (PUNCT) --[punct]--> is (AUX)`
512
513	`Sentence 6: Now, Japan is a relatively significant supplier of steel to the U.S., and of course it's an American ally.`
514	`Now (ADV) --[advmod]--> is (AUX)`
515	`, (PUNCT) --[punct]--> is (AUX)`
516	`Japan (PROPN) --[nsubj]--> is (AUX)`
517	`is (AUX) --[ROOT]--> is (AUX)`
518	`a (DET) --[det]--> supplier (NOUN)`
519	`relatively (ADV) --[advmod]--> significant (ADJ)`
520	`significant (ADJ) --[amod]--> supplier (NOUN)`
521	`supplier (NOUN) --[attr]--> is (AUX)`
522	`of (ADP) --[prep]--> supplier (NOUN)`
523	`steel (NOUN) --[pobj]--> of (ADP)`
524	`to (ADP) --[prep]--> supplier (NOUN)`
525	`the (DET) --[det]--> U.S. (PROPN)`
526	`U.S. (PROPN) --[pobj]--> to (ADP)`
527	`, (PUNCT) --[punct]--> is (AUX)`
528	`and (CCONJ) --[cc]--> is (AUX)`
529	`of (ADP) --[prep]--> 's (AUX)`
530	`course (NOUN) --[pobj]--> of (ADP)`
531	`it (PRON) --[nsubj]--> 's (AUX)`
532	`'s (AUX) --[conj]--> is (AUX)`
533	`an (DET) --[det]--> ally (NOUN)`
534	`American (ADJ) --[amod]--> ally (NOUN)`
535	`ally (NOUN) --[attr]--> 's (AUX)`
536	`. (PUNCT) --[punct]--> 's (AUX)`
537
538	`Sentence 7: But Japan was never given one of those temporary waivers like those other countries, so customers of Japanese steel and aluminum have been seeing a tariff - 25 percent on steel, 10 percent on aluminum - since late March.`
539	`But (CCONJ) --[cc]--> given (VERB)`
540	`Japan (PROPN) --[nsubjpass]--> given (VERB)`
541	`was (AUX) --[auxpass]--> given (VERB)`
542	`never (ADV) --[neg]--> given (VERB)`
543	`given (VERB) --[ccomp]--> seeing (VERB)`
544	`one (NUM) --[dobj]--> given (VERB)`
545	`of (ADP) --[prep]--> one (NUM)`
546	`those (DET) --[det]--> waivers (NOUN)`
547	`temporary (ADJ) --[amod]--> waivers (NOUN)`
548	`waivers (NOUN) --[pobj]--> of (ADP)`
549	`like (ADP) --[prep]--> waivers (NOUN)`
550	`those (DET) --[det]--> countries (NOUN)`
551	`other (ADJ) --[amod]--> countries (NOUN)`
552	`countries (NOUN) --[pobj]--> like (ADP)`
553	`, (PUNCT) --[punct]--> given (VERB)`
554	`so (SCONJ) --[mark]--> seeing (VERB)`
555	`customers (NOUN) --[nsubj]--> seeing (VERB)`
556	`of (ADP) --[prep]--> customers (NOUN)`
557	`Japanese (ADJ) --[amod]--> steel (NOUN)`
558	`steel (NOUN) --[pobj]--> of (ADP)`
559	`and (CCONJ) --[cc]--> steel (NOUN)`
560	`aluminum (NOUN) --[conj]--> steel (NOUN)`
561	`have (AUX) --[aux]--> seeing (VERB)`
562	`been (AUX) --[aux]--> seeing (VERB)`
563	`seeing (VERB) --[ROOT]--> seeing (VERB)`
564	`a (DET) --[det]--> percent (NOUN)`
565	`tariff (NOUN) --[compound]--> 25 (NUM)`
566	`- (PUNCT) --[punct]--> 25 (NUM)`
567	`25 (NUM) --[nummod]--> percent (NOUN)`
568	`percent (NOUN) --[dobj]--> seeing (VERB)`
569	`on (ADP) --[prep]--> percent (NOUN)`
570	`steel (NOUN) --[pobj]--> on (ADP)`
571	`, (PUNCT) --[punct]--> percent (NOUN)`
572	`10 (NUM) --[nummod]--> percent (NOUN)`
573	`percent (NOUN) --[appos]--> percent (NOUN)`
574	`on (ADP) --[prep]--> percent (NOUN)`
575	`aluminum (NOUN) --[pobj]--> on (ADP)`
576	`- (PUNCT) --[punct]--> seeing (VERB)`
577	`since (SCONJ) --[prep]--> seeing (VERB)`
578	`late (ADJ) --[amod]--> March (PROPN)`
579	`March (PROPN) --[pobj]--> since (SCONJ)`
580	`. (PUNCT) --[punct]--> seeing (VERB)`
581
582	`Sentence 8: That was a source of some friction when Japanese Prime Minister Shinzo Abe was down in Florida meeting with President Trump two weeks ago.`
583	`That (PRON) --[nsubj]--> was (AUX)`
584	`was (AUX) --[ROOT]--> was (AUX)`
585	`a (DET) --[det]--> source (NOUN)`
586	`source (NOUN) --[attr]--> was (AUX)`
587	`of (ADP) --[prep]--> source (NOUN)`
588	`some (DET) --[det]--> friction (NOUN)`
589	`friction (NOUN) --[pobj]--> of (ADP)`
590	`when (SCONJ) --[advmod]--> was (AUX)`
591	`Japanese (ADJ) --[amod]--> Minister (PROPN)`
592	`Prime (PROPN) --[compound]--> Minister (PROPN)`
593	`Minister (PROPN) --[compound]--> Abe (PROPN)`
594	`Shinzo (PROPN) --[compound]--> Abe (PROPN)`
595	`Abe (PROPN) --[nsubj]--> was (AUX)`
596	`was (AUX) --[advcl]--> was (AUX)`
597	`down (ADV) --[advmod]--> was (AUX)`
598	`in (ADP) --[prep]--> down (ADV)`
599	`Florida (PROPN) --[compound]--> meeting (NOUN)`
600	`meeting (NOUN) --[pobj]--> in (ADP)`
601	`with (ADP) --[prep]--> meeting (NOUN)`
602	`President (PROPN) --[compound]--> Trump (PROPN)`
603	`Trump (PROPN) --[pobj]--> with (ADP)`
604	`two (NUM) --[nummod]--> weeks (NOUN)`
605	`weeks (NOUN) --[npadvmod]--> ago (ADV)`
606	`ago (ADV) --[advmod]--> was (AUX)`
607	`. (PUNCT) --[punct]--> was (AUX)`
608
609	`Sentence 9: Also, South Korea has been given a permanent exemption from the steel tariffs as part of a renegotiated free trade agreement.`
610	`Also (ADV) --[advmod]--> given (VERB)`
611	`, (PUNCT) --[punct]--> given (VERB)`
612	`South (PROPN) --[compound]--> Korea (PROPN)`
613	`Korea (PROPN) --[nsubjpass]--> given (VERB)`
614	`has (AUX) --[aux]--> given (VERB)`
615	`been (AUX) --[auxpass]--> given (VERB)`
616	`given (VERB) --[ROOT]--> given (VERB)`
617	`a (DET) --[det]--> exemption (NOUN)`
618	`permanent (ADJ) --[amod]--> exemption (NOUN)`
619	`exemption (NOUN) --[dobj]--> given (VERB)`
620	`from (ADP) --[prep]--> exemption (NOUN)`
621	`the (DET) --[det]--> tariffs (NOUN)`
622	`steel (NOUN) --[compound]--> tariffs (NOUN)`
623	`tariffs (NOUN) --[pobj]--> from (ADP)`
624	`as (ADP) --[prep]--> exemption (NOUN)`
625	`part (NOUN) --[pobj]--> as (ADP)`
626	`of (ADP) --[prep]--> part (NOUN)`
627	`a (DET) --[det]--> agreement (NOUN)`
628	`renegotiated (ADJ) --[amod]--> agreement (NOUN)`
629	`free (ADJ) --[amod]--> trade (NOUN)`
630	`trade (NOUN) --[compound]--> agreement (NOUN)`
631	`agreement (NOUN) --[pobj]--> of (ADP)`
632	`. (PUNCT) --[punct]--> given (VERB)`
633
634	`Sentence 10: In exchange, though, South Korea had to agree to cut its steel exports to the U.S. by about 30 percent.`
635	`In (ADP) --[prep]--> had (VERB)`
636	`exchange (NOUN) --[pobj]--> In (ADP)`
637	`, (PUNCT) --[punct]--> had (VERB)`
638	`though (ADV) --[advmod]--> had (VERB)`
639	`, (PUNCT) --[punct]--> had (VERB)`
640	`South (PROPN) --[compound]--> Korea (PROPN)`
641	`Korea (PROPN) --[nsubj]--> had (VERB)`
642	`had (VERB) --[ROOT]--> had (VERB)`
643	`to (PART) --[aux]--> agree (VERB)`
644	`agree (VERB) --[xcomp]--> had (VERB)`
645	`to (PART) --[aux]--> cut (VERB)`
646	`cut (VERB) --[xcomp]--> agree (VERB)`
647	`its (PRON) --[poss]--> exports (NOUN)`
648	`steel (NOUN) --[compound]--> exports (NOUN)`
649	`exports (NOUN) --[dobj]--> cut (VERB)`
650	`to (ADP) --[prep]--> cut (VERB)`
651	`the (DET) --[det]--> U.S. (PROPN)`
652	`U.S. (PROPN) --[pobj]--> to (ADP)`
653	`by (ADP) --[prep]--> cut (VERB)`
654	`about (ADV) --[advmod]--> 30 (NUM)`
655	`30 (NUM) --[nummod]--> percent (NOUN)`
656	`percent (NOUN) --[pobj]--> by (ADP)`
657	`. (PUNCT) --[punct]--> had (VERB)`
658
659	`Sentence 11: And the White House says it's looking to impose similar quotas on some of these other countries.`
660	`And (CCONJ) --[cc]--> says (VERB)`
661	`the (DET) --[det]--> House (PROPN)`
662	`White (PROPN) --[compound]--> House (PROPN)`
663	`House (PROPN) --[nsubj]--> says (VERB)`
664	`says (VERB) --[ROOT]--> says (VERB)`
665	`it (PRON) --[nsubj]--> looking (VERB)`
666	`'s (AUX) --[aux]--> looking (VERB)`
667	`looking (VERB) --[ccomp]--> says (VERB)`
668	`to (PART) --[aux]--> impose (VERB)`
669	`impose (VERB) --[xcomp]--> looking (VERB)`
670	`similar (ADJ) --[amod]--> quotas (NOUN)`
671	`quotas (NOUN) --[dobj]--> impose (VERB)`
672	`on (ADP) --[prep]--> impose (VERB)`
673	`some (PRON) --[pobj]--> on (ADP)`
674	`of (ADP) --[prep]--> some (PRON)`
675	`these (DET) --[det]--> countries (NOUN)`
676	`other (ADJ) --[amod]--> countries (NOUN)`
677	`countries (NOUN) --[pobj]--> of (ADP)`
678	`. (PUNCT) --[punct]--> says (VERB)`
679
680	`Total sentences: 11`
681
682	`RAKE Keyphrase Relationships:`
683	`noun phrase: "China" contains [china], [china]`
684	`noun phrase: "these tariffs" contains [tariffs], [tariffs], [tariff]`
685	`noun phrase: "China" contains [china], [china]`
686	`noun phrase: "a big supplier" contains [big supplier], [u], [u]`
687	`noun phrase: "steel" contains [steel], [steel], [steel]`
688	`noun phrase: "aluminum" contains [u], [u], [aluminum], [aluminum], [aluminum]`
689	`noun phrase: "the United States" contains [united states], [u], [u]`
690	`noun phrase: "the global glut" contains [global glut], [u], [u]`
691	`noun phrase: "those products" contains [u], [u], [products]`
692	`noun phrase: "Russia" contains [u], [u], [russia]`
693	`noun phrase: "the tariffs" contains [tariffs], [tariffs], [tariff]`
694	`noun phrase: "Japan" contains [japan], [japan], [japan]`
695	`noun phrase: "Japan" contains [japan], [japan], [japan]`
696	`noun phrase: "a relatively significant supplier" contains [relatively significant supplier], [u], [u]`
697	`noun phrase: "steel" contains [steel], [steel], [steel]`
698	`noun phrase: "the U.S." contains [u], [u]`
699	`noun phrase: "course" contains [u], [u], [course]`
700	`noun phrase: "Japan" contains [japan], [japan], [japan]`
701	`noun phrase: "those other countries" contains [u], [u], [countries], [countries]`
702	`noun phrase: "customers" contains [u], [u], [customers]`
703	`noun phrase: "Japanese steel" contains [japanese steel], [steel], [steel], [steel], [japan], [japan], [japan]`
704	`noun phrase: "aluminum" contains [u], [u], [aluminum], [aluminum], [aluminum]`
705	`noun phrase: "a tariff - 25 percent" contains [25 percent], [tariff]`
706	`noun phrase: "steel" contains [steel], [steel], [steel]`
707	`noun phrase: "aluminum" contains [u], [u], [aluminum], [aluminum], [aluminum]`
708	`noun phrase: "a source" contains [u], [u], [source]`
709	`noun phrase: "Japanese Prime Minister Shinzo Abe" contains [japan], [japan], [japan]`
710	`noun phrase: "President Trump" contains [u], [u]`
711	`noun phrase: "South Korea" contains [south korea], [south korea], [u], [u]`
712	`noun phrase: "the steel tariffs" contains [steel tariffs], [steel], [steel], [steel], [tariffs], [tariffs], [tariff]`
713	`noun phrase: "South Korea" contains [south korea], [south korea], [u], [u]`
714	`noun phrase: "its steel exports" contains [steel exports], [steel], [steel], [steel]`
715	`noun phrase: "the U.S." contains [u], [u]`
716	`noun phrase: "about 30 percent" contains [30 percent], [u], [u]`
717	`noun phrase: "the White House" contains [u], [u]`
718	`noun phrase: "similar quotas" contains [u], [u]`
719	`noun phrase: "these other countries" contains [u], [u], [countries], [countries]`
720	`verb phrase: "thought is" contains [u], [u], [thought], [though]`
721	`verb phrase: "driven 's prices" contains [prices], [driven]`
722	`verb phrase: "given Also has been exemption" contains [given], [also]`
723	`verb phrase: "had In though" contains [u], [u], [though]`
724	`verb phrase: "cut to exports to by" contains [u], [u], [cut]`
725	`verb phrase: "impose to quotas on" contains [u], [u]`
726	`Total relationships found: 43`
727
728	`YAKE Keyphrase Relationships:`
729	`noun phrase: "Japanese steel" contains [steel], [japan], [japanese]`
730	`noun phrase: "Japanese Prime Minister Shinzo Abe" contains [japanese prime minister], [prime minister shinzo], [minister shinzo abe], [japan], [japanese prime], [prime minister], [minister shinzo], [shinzo abe], [japanese]`
731	`noun phrase: "South Korea" contains [south korea], [south], [korea]`
732	`noun phrase: "the steel tariffs" contains [steel], [tariffs]`
733	`noun phrase: "South Korea" contains [south korea], [south], [korea]`
734	`Total relationships found: 5`
735
736	`Timing Comparison:`
737	`RAKE: 0.0000sec`
738	`YAKE: 0.0254sec`
739	`KeyBERT: 0.1860sec`
740	`Dependencies: 0.0253sec`
741	`Fastest: RAKE`
742
743	`================================================================================`
744	`Message 4:`
745	`SETH BENZ: We already know an example would be boreal chickadees - used to breed in the park as late as the mid-'90s.`
746	`--------------------------------------------------------------------------------`
747	`RAKE Keywords:`
748	`- mid -' 90s (score: 9.00)`
749	`- seth benz (score: 4.00) → SETH BENZ`
750	`- example would (score: 4.00)`
751	`- boreal chickadees (score: 4.00) → boreal chickadee`
752	`- already know (score: 4.00)`
753	`- used (score: 1.00) → use`
754	`- park (score: 1.00)`
755	`- late (score: 1.00)`
756	`- breed (score: 1.00)`
757	`Total keywords: 9 extracted in 0.0000 seconds`
758
759	`YAKE Keywords:`
760	`- SETH BENZ (score: 0.00) → SETH BENZ`
761	`- boreal chickadees (score: 0.02) → boreal chickadee`
762	`- park as late (score: 0.03)`
763	`- SETH (score: 0.06) → SETH`
764	`- BENZ (score: 0.06) → BENZ`
765	`- chickadees (score: 0.10) → chickadee`
766	`- mid (score: 0.10)`
767	`- boreal (score: 0.16)`
768	`- breed (score: 0.16)`
769	`- park (score: 0.16)`
770	`- late (score: 0.16)`
771	`Total keywords: 11 extracted in 0.0040 seconds`
772
773	`KeyBERT Keywords:`
774	`- example boreal chickadees (score: 0.66)`
775	`- chickadees used breed (score: 0.64)`
776	`- boreal chickadees (score: 0.64)`
777	`- boreal chickadees used (score: 0.63)`
778	`- chickadees used (score: 0.54)`
779	`- chickadees (score: 0.51)`
780	`- breed park late (score: 0.50)`
781	`- breed park (score: 0.49)`
782	`- used breed park (score: 0.48)`
783	`- example boreal (score: 0.40)`
784	`- late mid 90s (score: 0.39)`
785	`- know example boreal (score: 0.39)`
786	`- used breed (score: 0.38)`
787	`- breed (score: 0.36)`
788	`- boreal (score: 0.34)`
789	`- mid 90s (score: 0.33)`
790	`- park late mid (score: 0.30)`
791	`- park (score: 0.29)`
792	`- benz know example (score: 0.29)`
793	`- example (score: 0.28)`
794	`Total keywords: 20 extracted in 0.0367 seconds`
795
796	`Dependency Relations (extracted in 0.0081sec):`
797
798	`Sentence 1: SETH BENZ: We already know an example would be boreal chickadees - used to breed in the park as late as the mid-'90s.`
799	`SETH (PROPN) --[compound]--> BENZ (PROPN)`
800	`BENZ (PROPN) --[dep]--> know (VERB)`
801	`: (PUNCT) --[punct]--> know (VERB)`
802	`We (PRON) --[nsubj]--> know (VERB)`
803	`already (ADV) --[advmod]--> know (VERB)`
804	`know (VERB) --[ROOT]--> know (VERB)`
805	`an (DET) --[det]--> example (NOUN)`
806	`example (NOUN) --[nsubj]--> be (AUX)`
807	`would (AUX) --[aux]--> be (AUX)`
808	`be (AUX) --[ccomp]--> know (VERB)`
809	`boreal (ADJ) --[amod]--> chickadees (NOUN)`
810	`chickadees (NOUN) --[attr]--> be (AUX)`
811	`- (PUNCT) --[punct]--> chickadees (NOUN)`
812	`used (VERB) --[acl]--> chickadees (NOUN)`
813	`to (PART) --[aux]--> breed (VERB)`
814	`breed (VERB) --[xcomp]--> used (VERB)`
815	`in (ADP) --[prep]--> breed (VERB)`
816	`the (DET) --[det]--> park (NOUN)`
817	`park (NOUN) --[pobj]--> in (ADP)`
818	`as (ADV) --[advmod]--> late (ADV)`
819	`late (ADV) --[advmod]--> breed (VERB)`
820	`as (ADP) --[prep]--> late (ADV)`
821	`the (DET) --[det]--> mid-'90s (NOUN)`
822	`mid-'90s (NOUN) --[pobj]--> as (ADP)`
823	`. (PUNCT) --[punct]--> know (VERB)`
824
825	`Total sentences: 1`
826
827	`RAKE Keyphrase Relationships:`
828	`verb phrase: "breed to in late" contains [late], [breed]`
829	`Total relationships found: 1`
830
831	`YAKE Keyphrase Relationships:`
832	`noun phrase: "boreal chickadees" contains [boreal chickadees], [chickadees], [boreal]`
833	`verb phrase: "breed to in late" contains [breed], [late]`
834	`Total relationships found: 2`
835
836	`Timing Comparison:`
837	`RAKE: 0.0000sec`
838	`YAKE: 0.0040sec`
839	`KeyBERT: 0.0367sec`
840	`Dependencies: 0.0081sec`
841	`Fastest: RAKE`
842
843	`================================================================================`
844	`Message 5:`
845	`GADSBY: So I quit in the middle of a show, and then I stopped doing comedy because it was an exhausting show to perform. So there was a part of me that's like - would be nice to stop, but things have happened.`
846	`--------------------------------------------------------------------------------`
847	`RAKE Keywords:`
848	`- exhausting show (score: 3.50)`
849	`- show (score: 1.50)`
850	`- would (score: 1.00)`
851	`- things (score: 1.00) → thing`
852	`- stopped (score: 1.00) → stop`
853	`- stop (score: 1.00)`
854	`- quit (score: 1.00)`
855	`- perform (score: 1.00)`
856	`- part (score: 1.00)`
857	`- nice (score: 1.00)`
858	`- middle (score: 1.00)`
859	`- like (score: 1.00)`
860	`- happened (score: 1.00) → happen`
861	`- gadsby (score: 1.00) → GADSBY`
862	`- comedy (score: 1.00)`
863	`Total keywords: 15 extracted in 0.0020 seconds`
864
865	`YAKE Keywords:`
866	`- GADSBY (score: 0.04) → GADSBY`
867	`- stopped doing comedy (score: 0.06) → stop do comedy`
868	`- exhausting show (score: 0.07)`
869	`- show to perform (score: 0.10)`
870	`- perform (score: 0.15)`
871	`- show (score: 0.15)`
872	`- quit (score: 0.23)`
873	`- middle (score: 0.23)`
874	`- stopped (score: 0.23) → stop`
875	`- comedy (score: 0.23)`
876	`- exhausting (score: 0.23)`
877	`- nice to stop (score: 0.23)`
878	`- things have happened (score: 0.23) → thing have happen`
879	`- stop (score: 0.37)`
880	`- happened (score: 0.37) → happen`
881	`- part (score: 0.51)`
882	`- nice (score: 0.51)`
883	`- things (score: 0.51) → thing`
884	`Total keywords: 18 extracted in 0.0100 seconds`
885
886	`KeyBERT Keywords:`
887	`- gadsby quit (score: 0.71)`
888	`- stopped doing comedy (score: 0.66)`
889	`- gadsby quit middle (score: 0.66)`
890	`- gadsby (score: 0.54)`
891	`- doing comedy exhausting (score: 0.52)`
892	`- comedy exhausting perform (score: 0.50)`
893	`- comedy exhausting (score: 0.49)`
894	`- quit middle stopped (score: 0.43)`
895	`- doing comedy (score: 0.42)`
896	`- stop things happened (score: 0.41)`
897	`- comedy (score: 0.40)`
898	`- quit (score: 0.39)`
899	`- stop things (score: 0.39)`
900	`- stopped (score: 0.38)`
901	`- stopped doing (score: 0.36)`
902	`- quit middle (score: 0.34)`
903	`- middle stopped doing (score: 0.32)`
904	`- middle stopped (score: 0.31)`
905	`- nice stop things (score: 0.30)`
906	`- stop (score: 0.30)`
907	`Total keywords: 20 extracted in 0.0524 seconds`
908
909	`Dependency Relations (extracted in 0.0113sec):`
910
911	`Sentence 1: GADSBY:`
912	`GADSBY (PROPN) --[ROOT]--> GADSBY (PROPN)`
913	`: (PUNCT) --[punct]--> GADSBY (PROPN)`
914
915	`Sentence 2: So I quit in the middle of a show, and then I stopped doing comedy because it was an exhausting show to perform.`
916	`So (CCONJ) --[advmod]--> quit (VERB)`
917	`I (PRON) --[nsubj]--> quit (VERB)`
918	`quit (VERB) --[ROOT]--> quit (VERB)`
919	`in (ADP) --[prep]--> quit (VERB)`
920	`the (DET) --[det]--> middle (NOUN)`
921	`middle (NOUN) --[pobj]--> in (ADP)`
922	`of (ADP) --[prep]--> middle (NOUN)`
923	`a (DET) --[det]--> show (NOUN)`
924	`show (NOUN) --[pobj]--> of (ADP)`
925	`, (PUNCT) --[punct]--> quit (VERB)`
926	`and (CCONJ) --[cc]--> quit (VERB)`
927	`then (ADV) --[advmod]--> stopped (VERB)`
928	`I (PRON) --[nsubj]--> stopped (VERB)`
929	`stopped (VERB) --[conj]--> quit (VERB)`
930	`doing (VERB) --[xcomp]--> stopped (VERB)`
931	`comedy (NOUN) --[dobj]--> doing (VERB)`
932	`because (SCONJ) --[mark]--> was (AUX)`
933	`it (PRON) --[nsubj]--> was (AUX)`
934	`was (AUX) --[advcl]--> stopped (VERB)`
935	`an (DET) --[det]--> show (NOUN)`
936	`exhausting (ADJ) --[amod]--> show (NOUN)`
937	`show (NOUN) --[attr]--> was (AUX)`
938	`to (PART) --[aux]--> perform (VERB)`
939	`perform (VERB) --[relcl]--> show (NOUN)`
940	`. (PUNCT) --[punct]--> stopped (VERB)`
941
942	`Sentence 3: So there was a part of me that's like - would be nice to stop, but things have happened.`
943	`So (ADV) --[advmod]--> was (VERB)`
944	`there (PRON) --[expl]--> was (VERB)`
945	`was (VERB) --[ROOT]--> was (VERB)`
946	`a (DET) --[det]--> part (NOUN)`
947	`part (NOUN) --[attr]--> was (VERB)`
948	`of (ADP) --[prep]--> part (NOUN)`
949	`me (PRON) --[pobj]--> of (ADP)`
950	`that (PRON) --[nsubj]--> 's (AUX)`
951	`'s (AUX) --[relcl]--> part (NOUN)`
952	`like (INTJ) --[intj]--> 's (AUX)`
953	`- (PUNCT) --[punct]--> be (AUX)`
954	`would (AUX) --[aux]--> be (AUX)`
955	`be (AUX) --[conj]--> was (VERB)`
956	`nice (ADJ) --[acomp]--> be (AUX)`
957	`to (PART) --[aux]--> stop (VERB)`
958	`stop (VERB) --[xcomp]--> nice (ADJ)`
959	`, (PUNCT) --[punct]--> be (AUX)`
960	`but (CCONJ) --[cc]--> was (VERB)`
961	`things (NOUN) --[nsubj]--> happened (VERB)`
962	`have (AUX) --[aux]--> happened (VERB)`
963	`happened (VERB) --[conj]--> was (VERB)`
964	`. (PUNCT) --[punct]--> happened (VERB)`
965
966	`Total sentences: 3`
967
968	`RAKE Keyphrase Relationships:`
969	`noun phrase: "an exhausting show" contains [exhausting show], [show]`
970	`verb phrase: "stopped then" contains [stopped], [stop]`
971	`Total relationships found: 2`
972
973	`YAKE Keyphrase Relationships:`
974	`noun phrase: "an exhausting show" contains [exhausting show], [show], [exhausting]`
975	`verb phrase: "stopped then" contains [stopped], [stop]`
976	`Total relationships found: 2`
977
978	`Timing Comparison:`
979	`RAKE: 0.0020sec`
980	`YAKE: 0.0100sec`
981	`KeyBERT: 0.0524sec`
982	`Dependencies: 0.0113sec`
983	`Fastest: RAKE`
984
985	`================================================================================`
986	`Message 6:`
987	CHANG: That, of course, is physicist Stephen Hawking, who died earlier this month. That machine voice became Hawking's voice after a debilitating neurological disease took away his own ability to talk. Hawking used to joke about getting stuck with an American accent. But the truth is, the late scientist grew quite attached to it - so attached that he got a group of tech whizzes to take on the painstaking task of rescuing his voice. After nearly 30 years, the 1986 hardware that powered Stephen Hawking's voice was degrading. And one of the engineers who had to figure out how to save it was Eric Dorsey.Hi, Eric.
988	`--------------------------------------------------------------------------------`
989	`RAKE Keywords:`
990	`- nearly 30 years (score: 9.00) → nearly 30 year`
991	`- powered stephen hawking (score: 8.67) → power Stephen Hawking`
992	`- physicist stephen hawking (score: 8.67) → physicist Stephen Hawking`
993	`- hawking used (score: 4.67) → hawk use`
994	`- tech whizzes (score: 4.00) → tech whizz`
995	`- painstaking task (score: 4.00)`
996	`- getting stuck (score: 4.00) → getting stick`
997	`- died earlier (score: 4.00) → die early`
998	`- american accent (score: 4.00)`
999	`- 1986 hardware (score: 4.00)`
1000	`- eric dorsey (score: 3.50) → Eric Dorsey`
1001	`- eric (score: 1.50) → Eric`
1002	`- voice (score: 1.00)`
1003	`- voice (score: 1.00)`
1004	`- voice (score: 1.00)`
1005	`- truth (score: 1.00)`
1006	`- talk (score: 1.00)`
1007	`- take (score: 1.00)`
1008	`- save (score: 1.00)`
1009	`- rescuing (score: 1.00) → rescue`
1010	`- one (score: 1.00)`
1011	`- month (score: 1.00)`
1012	`- joke (score: 1.00)`
1013	`- hi (score: 1.00)`
1014	`- group (score: 1.00)`
1015	`- got (score: 1.00) → get`
1016	`- figure (score: 1.00)`
1017	`- engineers (score: 1.00) → engineer`
1018	`- degrading (score: 1.00)`
1019	`- course (score: 1.00)`
1020	`- chang (score: 1.00)`
1021	`- attached (score: 1.00)`
1022	`- ability (score: 1.00)`
1023	`Total keywords: 33 extracted in 0.0000 seconds`
1024
1025	`YAKE Keywords:`
1026	`- physicist Stephen Hawking (score: 0.01) → physicist Stephen Hawking`
1027	`- Stephen Hawking (score: 0.03) → Stephen Hawking`
1028	`- earlier this month (score: 0.03) → early this month`
1029	`- Stephen Hawking voice (score: 0.04)`
1030	`- died earlier (score: 0.04) → die early`
1031	`- powered Stephen Hawking (score: 0.05) → power Stephen Hawking`
1032	`- physicist Stephen (score: 0.05) → physicist Stephen`
1033	`- CHANG (score: 0.05)`
1034	`- Hawking (score: 0.06) → Hawking`
1035	`- Hawking voice (score: 0.06)`
1036	`- Stephen (score: 0.13) → Stephen`
1037	`- voice (score: 0.13)`
1038	`- month (score: 0.16)`
1039	`- American accent (score: 0.17)`
1040	`- debilitating neurological disease (score: 0.17) → debilitate neurological disease`
1041	`- powered Stephen (score: 0.19) → power Stephen`
1042	`- Eric (score: 0.20) → Eric`
1043	`- physicist (score: 0.20)`
1044	`- died (score: 0.20) → die`
1045	`- earlier (score: 0.20) → early`
1046	`Total keywords: 20 extracted in 0.0272 seconds`
1047
1048	`KeyBERT Keywords:`
1049	`- stephen hawking voice (score: 0.71)`
1050	`- machine voice hawking (score: 0.66)`
1051	`- hawking voice degrading (score: 0.66)`
1052	`- voice hawking voice (score: 0.66)`
1053	`- hawking voice debilitating (score: 0.65)`
1054	`- hawking voice (score: 0.63)`
1055	`- voice hawking (score: 0.62)`
1056	`- machine voice (score: 0.57)`
1057	`- rescuing voice (score: 0.54)`
1058	`- stephen hawking died (score: 0.53)`
1059	`- voice degrading engineers (score: 0.53)`
1060	`- rescuing voice nearly (score: 0.53)`
1061	`- voice debilitating (score: 0.52)`
1062	`- voice debilitating neurological (score: 0.52)`
1063	`- voice (score: 0.52)`
1064	`- stephen hawking (score: 0.51)`
1065	`- physicist stephen hawking (score: 0.51)`
1066	`- ability talk hawking (score: 0.50)`
1067	`- voice degrading (score: 0.50)`
1068	`- task rescuing voice (score: 0.50)`
1069	`Total keywords: 20 extracted in 0.1110 seconds`
1070
1071	`Dependency Relations (extracted in 0.0220sec):`
1072
1073	`Sentence 1: CHANG: That, of course, is physicist Stephen Hawking, who died earlier this month.`
1074	`CHANG (NOUN) --[ROOT]--> CHANG (NOUN)`
1075	`: (PUNCT) --[punct]--> CHANG (NOUN)`
1076	`That (SCONJ) --[nsubj]--> is (AUX)`
1077	`, (PUNCT) --[punct]--> is (AUX)`
1078	`of (ADP) --[prep]--> is (AUX)`
1079	`course (NOUN) --[pobj]--> of (ADP)`
1080	`, (PUNCT) --[punct]--> is (AUX)`
1081	`is (AUX) --[acl]--> CHANG (NOUN)`
1082	`physicist (NOUN) --[compound]--> Hawking (PROPN)`
1083	`Stephen (PROPN) --[compound]--> Hawking (PROPN)`
1084	`Hawking (PROPN) --[attr]--> is (AUX)`
1085	`, (PUNCT) --[punct]--> Hawking (PROPN)`
1086	`who (PRON) --[nsubj]--> died (VERB)`
1087	`died (VERB) --[relcl]--> Hawking (PROPN)`
1088	`earlier (ADV) --[advmod]--> month (NOUN)`
1089	`this (DET) --[det]--> month (NOUN)`
1090	`month (NOUN) --[npadvmod]--> died (VERB)`
1091	`. (PUNCT) --[punct]--> CHANG (NOUN)`
1092
1093	`Sentence 2: That machine voice became Hawking's voice after a debilitating neurological disease took away his own ability to talk.`
1094	`That (DET) --[det]--> voice (NOUN)`
1095	`machine (NOUN) --[compound]--> voice (NOUN)`
1096	`voice (NOUN) --[nsubj]--> became (VERB)`
1097	`became (VERB) --[ROOT]--> became (VERB)`
1098	`Hawking (PROPN) --[poss]--> voice (NOUN)`
1099	`'s (PART) --[case]--> Hawking (PROPN)`
1100	`voice (NOUN) --[attr]--> became (VERB)`
1101	`after (SCONJ) --[mark]--> took (VERB)`
1102	`a (DET) --[det]--> disease (NOUN)`
1103	`debilitating (VERB) --[amod]--> disease (NOUN)`
1104	`neurological (ADJ) --[amod]--> disease (NOUN)`
1105	`disease (NOUN) --[nsubj]--> took (VERB)`
1106	`took (VERB) --[advcl]--> became (VERB)`
1107	`away (ADV) --[advmod]--> took (VERB)`
1108	`his (PRON) --[poss]--> ability (NOUN)`
1109	`own (ADJ) --[amod]--> ability (NOUN)`
1110	`ability (NOUN) --[dobj]--> took (VERB)`
1111	`to (PART) --[aux]--> talk (VERB)`
1112	`talk (VERB) --[acl]--> ability (NOUN)`
1113	`. (PUNCT) --[punct]--> became (VERB)`
1114
1115	`Sentence 3: Hawking used to joke about getting stuck with an American accent.`
1116	`Hawking (VERB) --[ROOT]--> Hawking (VERB)`
1117	`used (VERB) --[acl]--> Hawking (VERB)`
1118	`to (PART) --[aux]--> joke (VERB)`
1119	`joke (VERB) --[xcomp]--> used (VERB)`
1120	`about (ADP) --[prep]--> joke (VERB)`
1121	`getting (AUX) --[auxpass]--> stuck (VERB)`
1122	`stuck (VERB) --[pcomp]--> about (ADP)`
1123	`with (ADP) --[prep]--> stuck (VERB)`
1124	`an (DET) --[det]--> accent (NOUN)`
1125	`American (ADJ) --[amod]--> accent (NOUN)`
1126	`accent (NOUN) --[pobj]--> with (ADP)`
1127	`. (PUNCT) --[punct]--> Hawking (VERB)`
1128
1129	`Sentence 4: But the truth is, the late scientist grew quite attached to it - so attached that he got a group of tech whizzes to take on the painstaking task of rescuing his voice.`
1130	`But (CCONJ) --[cc]--> is (AUX)`
1131	`the (DET) --[det]--> truth (NOUN)`
1132	`truth (NOUN) --[nsubj]--> is (AUX)`
1133	`is (AUX) --[ROOT]--> is (AUX)`
1134	`, (PUNCT) --[punct]--> is (AUX)`
1135	`the (DET) --[det]--> scientist (NOUN)`
1136	`late (ADJ) --[amod]--> scientist (NOUN)`
1137	`scientist (NOUN) --[nsubj]--> grew (VERB)`
1138	`grew (VERB) --[ccomp]--> is (AUX)`
1139	`quite (ADV) --[advmod]--> attached (ADJ)`
1140	`attached (ADJ) --[acomp]--> grew (VERB)`
1141	`to (ADP) --[prep]--> attached (ADJ)`
1142	`it (PRON) --[pobj]--> to (ADP)`
1143	`- (PUNCT) --[punct]--> is (AUX)`
1144	`so (ADV) --[advmod]--> attached (VERB)`
1145	`attached (VERB) --[acomp]--> is (AUX)`
1146	`that (SCONJ) --[mark]--> got (VERB)`
1147	`he (PRON) --[nsubj]--> got (VERB)`
1148	`got (VERB) --[ccomp]--> attached (VERB)`
1149	`a (DET) --[det]--> group (NOUN)`
1150	`group (NOUN) --[dobj]--> got (VERB)`
1151	`of (ADP) --[prep]--> group (NOUN)`
1152	`tech (NOUN) --[compound]--> whizzes (NOUN)`
1153	`whizzes (NOUN) --[pobj]--> of (ADP)`
1154	`to (PART) --[aux]--> take (VERB)`
1155	`take (VERB) --[advcl]--> got (VERB)`
1156	`on (ADP) --[prt]--> take (VERB)`
1157	`the (DET) --[det]--> task (NOUN)`
1158	`painstaking (ADJ) --[amod]--> task (NOUN)`
1159	`task (NOUN) --[dobj]--> take (VERB)`
1160	`of (ADP) --[prep]--> task (NOUN)`
1161	`rescuing (VERB) --[pcomp]--> of (ADP)`
1162	`his (PRON) --[poss]--> voice (NOUN)`
1163	`voice (NOUN) --[dobj]--> rescuing (VERB)`
1164	`. (PUNCT) --[punct]--> is (AUX)`
1165
1166	`Sentence 5: After nearly 30 years, the 1986 hardware that powered Stephen Hawking's voice was degrading.`
1167	`After (ADP) --[prep]--> degrading (ADJ)`
1168	`nearly (ADV) --[advmod]--> 30 (NUM)`
1169	`30 (NUM) --[nummod]--> years (NOUN)`
1170	`years (NOUN) --[pobj]--> After (ADP)`
1171	`, (PUNCT) --[punct]--> degrading (ADJ)`
1172	`the (DET) --[det]--> hardware (NOUN)`
1173	`1986 (NUM) --[nummod]--> hardware (NOUN)`
1174	`hardware (NOUN) --[nsubj]--> degrading (ADJ)`
1175	`that (PRON) --[nsubj]--> powered (VERB)`
1176	`powered (VERB) --[relcl]--> hardware (NOUN)`
1177	`Stephen (PROPN) --[compound]--> Hawking (PROPN)`
1178	`Hawking (PROPN) --[poss]--> voice (NOUN)`
1179	`'s (PART) --[case]--> Hawking (PROPN)`
1180	`voice (NOUN) --[dobj]--> powered (VERB)`
1181	`was (AUX) --[aux]--> degrading (ADJ)`
1182	`degrading (ADJ) --[ROOT]--> degrading (ADJ)`
1183	`. (PUNCT) --[punct]--> degrading (ADJ)`
1184
1185	`Sentence 6: And one of the engineers who had to figure out how to save it was Eric Dorsey.`
1186	`And (CCONJ) --[cc]--> was (AUX)`
1187	`one (NUM) --[nsubj]--> was (AUX)`
1188	`of (ADP) --[prep]--> one (NUM)`
1189	`the (DET) --[det]--> engineers (NOUN)`
1190	`engineers (NOUN) --[pobj]--> of (ADP)`
1191	`who (PRON) --[nsubj]--> had (VERB)`
1192	`had (VERB) --[relcl]--> engineers (NOUN)`
1193	`to (PART) --[aux]--> figure (VERB)`
1194	`figure (VERB) --[xcomp]--> had (VERB)`
1195	`out (ADP) --[prt]--> figure (VERB)`
1196	`how (SCONJ) --[advmod]--> save (VERB)`
1197	`to (PART) --[aux]--> save (VERB)`
1198	`save (VERB) --[xcomp]--> figure (VERB)`
1199	`it (PRON) --[nsubj]--> was (AUX)`
1200	`was (AUX) --[ROOT]--> was (AUX)`
1201	`Eric (PROPN) --[compound]--> Dorsey (PROPN)`
1202	`Dorsey (PROPN) --[attr]--> was (AUX)`
1203	`. (PUNCT) --[punct]--> was (AUX)`
1204
1205	`Sentence 7: Hi, Eric.`
1206	`Hi (INTJ) --[ROOT]--> Hi (INTJ)`
1207	`, (PUNCT) --[punct]--> Hi (INTJ)`
1208	`Eric (PROPN) --[npadvmod]--> Hi (INTJ)`
1209	`. (PUNCT) --[punct]--> Hi (INTJ)`
1210
1211	`Total sentences: 7`
1212
1213	`RAKE Keyphrase Relationships:`
1214	`noun phrase: "That machine voice" contains [voice], [voice], [voice], [hi]`
1215	`noun phrase: "Hawking's voice" contains [voice], [voice], [voice]`
1216	`noun phrase: "his own ability" contains [hi], [ability]`
1217	`noun phrase: "an American accent" contains [american accent], [eric]`
1218	`noun phrase: "tech whizzes" contains [tech whizzes], [hi]`
1219	`noun phrase: "his voice" contains [voice], [voice], [voice], [hi]`
1220	`noun phrase: "Stephen Hawking's voice" contains [voice], [voice], [voice]`
1221	`noun phrase: "Eric Dorsey" contains [eric dorsey], [eric]`
1222	`verb phrase: "became voice" contains [voice], [voice], [voice]`
1223	`verb phrase: "got group" contains [group], [got]`
1224	`verb phrase: "rescuing voice" contains [voice], [voice], [voice], [rescuing]`
1225	`verb phrase: "powered voice" contains [voice], [voice], [voice]`
1226	`Total relationships found: 12`
1227
1228	`YAKE Keyphrase Relationships:`
1229	`noun phrase: "physicist Stephen Hawking" contains [physicist stephen hawking], [stephen hawking], [physicist stephen], [hawking], [stephen], [physicist]`
1230	`noun phrase: "Hawking's voice" contains [hawking], [voice]`
1231	`noun phrase: "an American accent" contains [american accent], [eric]`
1232	`noun phrase: "Stephen Hawking's voice" contains [stephen hawking], [hawking], [stephen], [voice]`
1233	`Total relationships found: 4`
1234
1235	`Timing Comparison:`
1236	`RAKE: 0.0000sec`
1237	`YAKE: 0.0272sec`
1238	`KeyBERT: 0.1110sec`
1239	`Dependencies: 0.0220sec`
1240	`Fastest: RAKE`
1241
1242	`================================================================================`
1243	`Message 7:`
1244	`WALLACH: So it has been hard...`
1245	`--------------------------------------------------------------------------------`
1246	`RAKE Keywords:`
1247	`- hard ... (score: 4.00)`
1248	`- wallach (score: 1.00) → WALLACH`
1249	`Total keywords: 2 extracted in 0.0000 seconds`
1250
1251	`YAKE Keywords:`
1252	`- WALLACH (score: 0.03) → WALLACH`
1253	`- hard (score: 0.16)`
1254	`Total keywords: 2 extracted in 0.0000 seconds`
1255
1256	`KeyBERT Keywords:`
1257	`- wallach hard (score: 0.79)`
1258	`- wallach (score: 0.72)`
1259	`- hard (score: 0.46)`
1260	`Total keywords: 3 extracted in 0.0159 seconds`
1261
1262	`Dependency Relations (extracted in 0.0040sec):`
1263
1264	`Sentence 1: WALLACH:`
1265	`WALLACH (PROPN) --[ROOT]--> WALLACH (PROPN)`
1266	`: (PUNCT) --[punct]--> WALLACH (PROPN)`
1267
1268	`Sentence 2: So it has been hard...`
1269	`So (CCONJ) --[advmod]--> been (AUX)`
1270	`it (PRON) --[nsubj]--> been (AUX)`
1271	`has (AUX) --[aux]--> been (AUX)`
1272	`been (AUX) --[ROOT]--> been (AUX)`
1273	`hard (ADJ) --[acomp]--> been (AUX)`
1274	`... (PUNCT) --[punct]--> been (AUX)`
1275
1276	`Total sentences: 2`
1277
1278	`RAKE Keyphrase Relationships:`
1279	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
1280
1281	`YAKE Keyphrase Relationships:`
1282	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
1283
1284	`Timing Comparison:`
1285	`RAKE: 0.0000sec`
1286	`YAKE: 0.0000sec`
1287	`KeyBERT: 0.0159sec`
1288	`Dependencies: 0.0040sec`
1289	`Fastest: RAKE`
1290
1291	`================================================================================`
1292	`Message 8:`
1293	BULOS: Well, so it started off with them being hesitant in the beginning. And then, at some point in time, you know, you just heard gunfire, right? There was just an eruption of shooting everywhere. And this was happening as we were walking through the gate and approaching what had been, moments before, the American-controlled section of that military airbase. And then we finally get to the gate, and they walk through and - you know, I mean, look. I mean, it was almost like sporting at some level, I guess is one way to describe it, just looking at what now was theirs. You know, they're - at what was left behind, I guess.
1294	`--------------------------------------------------------------------------------`
1295	`RAKE Keywords:`
1296	`- almost like sporting (score: 9.00)`
1297	`- shooting everywhere (score: 4.00) → shoot everywhere`
1298	`- one way (score: 4.00)`
1299	`- military airbase (score: 4.00)`
1300	`- left behind (score: 4.00) → leave behind`
1301	`- heard gunfire (score: 4.00) → hear gunfire`
1302	`- finally get (score: 4.00)`
1303	`- controlled section (score: 4.00) → control section`
1304	`- well (score: 1.00)`
1305	`- walking (score: 1.00) → walk`
1306	`- walk (score: 1.00)`
1307	`- time (score: 1.00)`
1308	`- started (score: 1.00) → start`
1309	`- right (score: 1.00)`
1310	`- point (score: 1.00)`
1311	`- moments (score: 1.00) → moment`
1312	`- mean (score: 1.00)`
1313	`- mean (score: 1.00)`
1314	`- looking (score: 1.00) → look`
1315	`- look (score: 1.00)`
1316	`- level (score: 1.00)`
1317	`- know (score: 1.00)`
1318	`- know (score: 1.00)`
1319	`- know (score: 1.00)`
1320	`- hesitant (score: 1.00)`
1321	`- happening (score: 1.00) → happen`
1322	`- guess (score: 1.00)`
1323	`- guess (score: 1.00)`
1324	`- gate (score: 1.00)`
1325	`- gate (score: 1.00)`
1326	`- eruption (score: 1.00)`
1327	`- describe (score: 1.00)`
1328	`- bulos (score: 1.00) → BULOS`
1329	`- beginning (score: 1.00)`
1330	`- approaching (score: 1.00) → approach`
1331	`- american (score: 1.00) → American`
1332	`Total keywords: 36 extracted in 0.0000 seconds`
1333
1334	`YAKE Keywords:`
1335	`- BULOS (score: 0.05) → BULOS`
1336	`- beginning (score: 0.13)`
1337	`- point in time (score: 0.15)`
1338	`- heard gunfire (score: 0.15) → hear gunfire`
1339	`- started (score: 0.16) → start`
1340	`- hesitant (score: 0.16)`
1341	`- American-controlled section (score: 0.21)`
1342	`- gate (score: 0.22)`
1343	`- guess (score: 0.25)`
1344	`- eruption of shooting (score: 0.31) → eruption of shoot`
1345	`- American-controlled (score: 0.32)`
1346	`- time (score: 0.33)`
1347	`- gunfire (score: 0.33)`
1348	`- military airbase (score: 0.34)`
1349	`- point (score: 0.40)`
1350	`- heard (score: 0.40) → hear`
1351	`- moments (score: 0.47) → moment`
1352	`- airbase (score: 0.47)`
1353	`- eruption (score: 0.49)`
1354	`- shooting (score: 0.49) → shoot`
1355	`Total keywords: 20 extracted in 0.0200 seconds`
1356
1357	`KeyBERT Keywords:`
1358	`- military airbase finally (score: 0.49)`
1359	`- bulos started hesitant (score: 0.48)`
1360	`- military airbase (score: 0.45)`
1361	`- section military airbase (score: 0.44)`
1362	`- heard gunfire (score: 0.42)`
1363	`- shooting happening (score: 0.41)`
1364	`- bulos started (score: 0.41)`
1365	`- eruption shooting happening (score: 0.40)`
1366	`- gunfire (score: 0.38)`
1367	`- eruption shooting (score: 0.38)`
1368	`- just heard gunfire (score: 0.38)`
1369	`- heard gunfire right (score: 0.37)`
1370	`- just eruption shooting (score: 0.34)`
1371	`- gunfire right (score: 0.34)`
1372	`- controlled section military (score: 0.34)`
1373	`- section military (score: 0.33)`
1374	`- bulos (score: 0.32)`
1375	`- airbase finally gate (score: 0.32)`
1376	`- shooting (score: 0.32)`
1377	`- shooting happening walking (score: 0.31)`
1378	`Total keywords: 20 extracted in 0.0763 seconds`
1379
1380	`Dependency Relations (extracted in 0.0237sec):`
1381
1382	`Sentence 1: BULOS:`
1383	`BULOS (PROPN) --[ROOT]--> BULOS (PROPN)`
1384	`: (PUNCT) --[punct]--> BULOS (PROPN)`
1385
1386	`Sentence 2: Well, so it started off with them being hesitant in the beginning.`
1387	`Well (INTJ) --[intj]--> started (VERB)`
1388	`, (PUNCT) --[punct]--> started (VERB)`
1389	`so (ADV) --[advmod]--> started (VERB)`
1390	`it (PRON) --[nsubj]--> started (VERB)`
1391	`started (VERB) --[ROOT]--> started (VERB)`
1392	`off (ADP) --[prt]--> started (VERB)`
1393	`with (ADP) --[prep]--> started (VERB)`
1394	`them (PRON) --[nsubj]--> being (AUX)`
1395	`being (AUX) --[pcomp]--> with (ADP)`
1396	`hesitant (ADJ) --[acomp]--> being (AUX)`
1397	`in (ADP) --[prep]--> being (AUX)`
1398	`the (DET) --[det]--> beginning (NOUN)`
1399	`beginning (NOUN) --[pobj]--> in (ADP)`
1400	`. (PUNCT) --[punct]--> started (VERB)`
1401
1402	`Sentence 3: And then, at some point in time, you know, you just heard gunfire, right?`
1403	`And (CCONJ) --[cc]--> heard (VERB)`
1404	`then (ADV) --[advmod]--> heard (VERB)`
1405	`, (PUNCT) --[punct]--> heard (VERB)`
1406	`at (ADP) --[prep]--> heard (VERB)`
1407	`some (DET) --[det]--> point (NOUN)`
1408	`point (NOUN) --[pobj]--> at (ADP)`
1409	`in (ADP) --[prep]--> point (NOUN)`
1410	`time (NOUN) --[pobj]--> in (ADP)`
1411	`, (PUNCT) --[punct]--> heard (VERB)`
1412	`you (PRON) --[nsubj]--> know (VERB)`
1413	`know (VERB) --[parataxis]--> heard (VERB)`
1414	`, (PUNCT) --[punct]--> heard (VERB)`
1415	`you (PRON) --[nsubj]--> heard (VERB)`
1416	`just (ADV) --[advmod]--> heard (VERB)`
1417	`heard (VERB) --[ccomp]--> right (ADJ)`
1418	`gunfire (NOUN) --[dobj]--> heard (VERB)`
1419	`, (PUNCT) --[punct]--> right (ADJ)`
1420	`right (ADJ) --[ROOT]--> right (ADJ)`
1421	`? (PUNCT) --[punct]--> right (ADJ)`
1422
1423	`Sentence 4: There was just an eruption of shooting everywhere.`
1424	`There (PRON) --[expl]--> was (VERB)`
1425	`was (VERB) --[ROOT]--> was (VERB)`
1426	`just (ADV) --[advmod]--> eruption (NOUN)`
1427	`an (DET) --[det]--> eruption (NOUN)`
1428	`eruption (NOUN) --[attr]--> was (VERB)`
1429	`of (ADP) --[prep]--> eruption (NOUN)`
1430	`shooting (VERB) --[pcomp]--> of (ADP)`
1431	`everywhere (ADV) --[advmod]--> shooting (VERB)`
1432	`. (PUNCT) --[punct]--> was (VERB)`
1433
1434	`Sentence 5: And this was happening as we were walking through the gate and approaching what had been, moments before, the American-controlled section of that military airbase.`
1435	`And (CCONJ) --[cc]--> happening (VERB)`
1436	`this (PRON) --[nsubj]--> happening (VERB)`
1437	`was (AUX) --[aux]--> happening (VERB)`
1438	`happening (VERB) --[ROOT]--> happening (VERB)`
1439	`as (SCONJ) --[mark]--> walking (VERB)`
1440	`we (PRON) --[nsubj]--> walking (VERB)`
1441	`were (AUX) --[aux]--> walking (VERB)`
1442	`walking (VERB) --[advcl]--> happening (VERB)`
1443	`through (ADP) --[prep]--> walking (VERB)`
1444	`the (DET) --[det]--> gate (NOUN)`
1445	`gate (NOUN) --[pobj]--> through (ADP)`
1446	`and (CCONJ) --[cc]--> walking (VERB)`
1447	`approaching (VERB) --[conj]--> walking (VERB)`
1448	`what (PRON) --[nsubj]--> been (AUX)`
1449	`had (AUX) --[aux]--> been (AUX)`
1450	`been (AUX) --[ccomp]--> approaching (VERB)`
1451	`, (PUNCT) --[punct]--> approaching (VERB)`
1452	`moments (NOUN) --[npadvmod]--> approaching (VERB)`
1453	`before (ADV) --[advmod]--> moments (NOUN)`
1454	`, (PUNCT) --[punct]--> happening (VERB)`
1455	`the (DET) --[det]--> section (NOUN)`
1456	`American (PROPN) --[npadvmod]--> controlled (VERB)`
1457	`- (PUNCT) --[punct]--> controlled (VERB)`
1458	`controlled (VERB) --[amod]--> section (NOUN)`
1459	`section (NOUN) --[dobj]--> happening (VERB)`
1460	`of (ADP) --[prep]--> section (NOUN)`
1461	`that (DET) --[det]--> airbase (NOUN)`
1462	`military (ADJ) --[amod]--> airbase (NOUN)`
1463	`airbase (NOUN) --[pobj]--> of (ADP)`
1464	`. (PUNCT) --[punct]--> happening (VERB)`
1465
1466	`Sentence 6: And then we finally get to the gate, and they walk through and - you know, I mean, look.`
1467	`And (CCONJ) --[cc]--> get (VERB)`
1468	`then (ADV) --[advmod]--> get (VERB)`
1469	`we (PRON) --[nsubj]--> get (VERB)`
1470	`finally (ADV) --[advmod]--> get (VERB)`
1471	`get (VERB) --[ROOT]--> get (VERB)`
1472	`to (ADP) --[prep]--> get (VERB)`
1473	`the (DET) --[det]--> gate (NOUN)`
1474	`gate (NOUN) --[pobj]--> to (ADP)`
1475	`, (PUNCT) --[punct]--> get (VERB)`
1476	`and (CCONJ) --[cc]--> get (VERB)`
1477	`they (PRON) --[nsubj]--> walk (VERB)`
1478	`walk (VERB) --[conj]--> get (VERB)`
1479	`through (ADP) --[prep]--> walk (VERB)`
1480	`and (CCONJ) --[cc]--> walk (VERB)`
1481	`- (PUNCT) --[punct]--> look (VERB)`
1482	`you (PRON) --[nsubj]--> know (VERB)`
1483	`know (VERB) --[parataxis]--> look (VERB)`
1484	`, (PUNCT) --[punct]--> look (VERB)`
1485	`I (PRON) --[nsubj]--> mean (VERB)`
1486	`mean (VERB) --[parataxis]--> look (VERB)`
1487	`, (PUNCT) --[punct]--> look (VERB)`
1488	`look (VERB) --[conj]--> walk (VERB)`
1489	`. (PUNCT) --[punct]--> walk (VERB)`
1490
1491	`Sentence 7: I mean, it was almost like sporting at some level, I guess is one way to describe it, just looking at what now was theirs.`
1492	`I (PRON) --[nsubj]--> mean (VERB)`
1493	`mean (VERB) --[parataxis]--> was (AUX)`
1494	`, (PUNCT) --[punct]--> was (AUX)`
1495	`it (PRON) --[nsubj]--> was (AUX)`
1496	`was (AUX) --[ccomp]--> is (AUX)`
1497	`almost (ADV) --[advmod]--> like (ADP)`
1498	`like (ADP) --[prep]--> was (AUX)`
1499	`sporting (NOUN) --[pobj]--> like (ADP)`
1500	`at (ADP) --[prep]--> sporting (NOUN)`
1501	`some (DET) --[det]--> level (NOUN)`
1502	`level (NOUN) --[pobj]--> at (ADP)`
1503	`, (PUNCT) --[punct]--> is (AUX)`
1504	`I (PRON) --[nsubj]--> guess (VERB)`
1505	`guess (VERB) --[parataxis]--> is (AUX)`
1506	`is (AUX) --[ROOT]--> is (AUX)`
1507	`one (NUM) --[nummod]--> way (NOUN)`
1508	`way (NOUN) --[attr]--> is (AUX)`
1509	`to (PART) --[aux]--> describe (VERB)`
1510	`describe (VERB) --[relcl]--> way (NOUN)`
1511	`it (PRON) --[dobj]--> describe (VERB)`
1512	`, (PUNCT) --[punct]--> is (AUX)`
1513	`just (ADV) --[advmod]--> looking (VERB)`
1514	`looking (VERB) --[advcl]--> is (AUX)`
1515	`at (ADP) --[prep]--> looking (VERB)`
1516	`what (PRON) --[nsubj]--> was (AUX)`
1517	`now (ADV) --[advmod]--> was (AUX)`
1518	`was (AUX) --[pcomp]--> at (ADP)`
1519	`theirs (ADJ) --[attr]--> was (AUX)`
1520	`. (PUNCT) --[punct]--> is (AUX)`
1521
1522	`Sentence 8: You know, they're - at what was left behind, I guess.`
1523	`You (PRON) --[nsubj]--> know (VERB)`
1524	`know (VERB) --[parataxis]--> 're (AUX)`
1525	`, (PUNCT) --[punct]--> 're (AUX)`
1526	`they (PRON) --[nsubj]--> 're (AUX)`
1527	`'re (AUX) --[ccomp]--> guess (VERB)`
1528	`- (PUNCT) --[punct]--> 're (AUX)`
1529	`at (ADP) --[prep]--> 're (AUX)`
1530	`what (PRON) --[nsubjpass]--> left (VERB)`
1531	`was (AUX) --[auxpass]--> left (VERB)`
1532	`left (VERB) --[pcomp]--> at (ADP)`
1533	`behind (ADV) --[prt]--> left (VERB)`
1534	`, (PUNCT) --[punct]--> guess (VERB)`
1535	`I (PRON) --[nsubj]--> guess (VERB)`
1536	`guess (VERB) --[ROOT]--> guess (VERB)`
1537	`. (PUNCT) --[punct]--> guess (VERB)`
1538
1539	`Total sentences: 8`
1540
1541	`RAKE Keyphrase Relationships:`
1542	`noun phrase: "the gate" contains [gate], [gate]`
1543	`noun phrase: "the American-controlled section" contains [controlled section], [american]`
1544	`noun phrase: "the gate" contains [gate], [gate]`
1545	`verb phrase: "walking were through" contains [walking], [walk]`
1546	`verb phrase: "looking just at" contains [looking], [look]`
1547	`Total relationships found: 5`
1548
1549	`YAKE Keyphrase Relationships:`
1550	`noun phrase: "the American-controlled section" contains [american-controlled section], [american-controlled]`
1551	`noun phrase: "that military airbase" contains [military airbase], [airbase]`
1552	`verb phrase: "heard then at just gunfire" contains [gunfire], [heard]`
1553	`Total relationships found: 3`
1554
1555	`Timing Comparison:`
1556	`RAKE: 0.0000sec`
1557	`YAKE: 0.0200sec`
1558	`KeyBERT: 0.0763sec`
1559	`Dependencies: 0.0237sec`
1560	`Fastest: RAKE`
1561
1562	`================================================================================`
1563	`Message 9:`
1564	`LEBOWITZ: She had contempt for the rich. She had contempt for any kind of falsity.`
1565	`--------------------------------------------------------------------------------`
1566	`RAKE Keywords:`
1567	`- rich (score: 1.00)`
1568	`- lebowitz (score: 1.00) → LEBOWITZ`
1569	`- kind (score: 1.00)`
1570	`- falsity (score: 1.00)`
1571	`- contempt (score: 1.00)`
1572	`- contempt (score: 1.00)`
1573	`Total keywords: 6 extracted in 0.0000 seconds`
1574
1575	`YAKE Keywords:`
1576	`- LEBOWITZ (score: 0.04) → LEBOWITZ`
1577	`- contempt (score: 0.14)`
1578	`- rich (score: 0.16)`
1579	`- kind of falsity (score: 0.27)`
1580	`- falsity (score: 0.40)`
1581	`- kind (score: 0.54)`
1582	`Total keywords: 6 extracted in 0.0020 seconds`
1583
1584	`KeyBERT Keywords:`
1585	`- lebowitz contempt rich (score: 0.76)`
1586	`- lebowitz contempt (score: 0.68)`
1587	`- rich contempt (score: 0.58)`
1588	`- contempt rich (score: 0.54)`
1589	`- rich contempt kind (score: 0.54)`
1590	`- lebowitz (score: 0.53)`
1591	`- contempt rich contempt (score: 0.49)`
1592	`- contempt kind falsity (score: 0.48)`
1593	`- contempt (score: 0.39)`
1594	`- falsity (score: 0.38)`
1595	`- rich (score: 0.38)`
1596	`- contempt kind (score: 0.35)`
1597	`- kind falsity (score: 0.31)`
1598	`- kind (score: 0.08)`
1599	`Total keywords: 14 extracted in 0.0215 seconds`
1600
1601	`Dependency Relations (extracted in 0.0020sec):`
1602
1603	`Sentence 1: LEBOWITZ: She had contempt for the rich.`
1604	`LEBOWITZ (PROPN) --[dep]--> had (VERB)`
1605	`: (PUNCT) --[punct]--> had (VERB)`
1606	`She (PRON) --[nsubj]--> had (VERB)`
1607	`had (VERB) --[ROOT]--> had (VERB)`
1608	`contempt (NOUN) --[dobj]--> had (VERB)`
1609	`for (ADP) --[prep]--> contempt (NOUN)`
1610	`the (DET) --[det]--> rich (ADJ)`
1611	`rich (ADJ) --[pobj]--> for (ADP)`
1612	`. (PUNCT) --[punct]--> had (VERB)`
1613
1614	`Sentence 2: She had contempt for any kind of falsity.`
1615	`She (PRON) --[nsubj]--> had (VERB)`
1616	`had (VERB) --[ROOT]--> had (VERB)`
1617	`contempt (NOUN) --[dobj]--> had (VERB)`
1618	`for (ADP) --[prep]--> contempt (NOUN)`
1619	`any (DET) --[det]--> kind (NOUN)`
1620	`kind (NOUN) --[pobj]--> for (ADP)`
1621	`of (ADP) --[prep]--> kind (NOUN)`
1622	`falsity (NOUN) --[pobj]--> of (ADP)`
1623	`. (PUNCT) --[punct]--> had (VERB)`
1624
1625	`Total sentences: 2`
1626
1627	`RAKE Keyphrase Relationships:`
1628	`noun phrase: "contempt" contains [contempt], [contempt]`
1629	`noun phrase: "contempt" contains [contempt], [contempt]`
1630	`verb phrase: "had contempt" contains [contempt], [contempt]`
1631	`verb phrase: "had contempt" contains [contempt], [contempt]`
1632	`Total relationships found: 4`
1633
1634	`YAKE Keyphrase Relationships:`
1635	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
1636
1637	`Timing Comparison:`
1638	`RAKE: 0.0000sec`
1639	`YAKE: 0.0020sec`
1640	`KeyBERT: 0.0215sec`
1641	`Dependencies: 0.0020sec`
1642	`Fastest: RAKE`
1643
1644	`================================================================================`
1645	`Message 10:`
1646	`SUMMERS: Wow.`
1647	`--------------------------------------------------------------------------------`
1648	`RAKE Keywords:`
1649	`- wow (score: 1.00)`
1650	`- summers (score: 1.00) → summer`
1651	`Total keywords: 2 extracted in 0.0000 seconds`
1652
1653	`YAKE Keywords:`
1654	`- SUMMERS (score: 0.03) → summer`
1655	`- Wow (score: 0.03)`
1656	`Total keywords: 2 extracted in 0.0000 seconds`
1657
1658	`KeyBERT Keywords:`
1659	`- summers wow (score: 0.88)`
1660	`- summers (score: 0.79)`
1661	`- wow (score: 0.28)`
1662	`Total keywords: 3 extracted in 0.0105 seconds`
1663
1664	`Dependency Relations (extracted in 0.0057sec):`
1665
1666	`Sentence 1: SUMMERS:`
1667	`SUMMERS (NOUN) --[ROOT]--> SUMMERS (NOUN)`
1668	`: (PUNCT) --[punct]--> SUMMERS (NOUN)`
1669
1670	`Sentence 2: Wow.`
1671	`Wow (INTJ) --[ROOT]--> Wow (INTJ)`
1672	`. (PUNCT) --[punct]--> Wow (INTJ)`
1673
1674	`Total sentences: 2`
1675
1676	`RAKE Keyphrase Relationships:`
1677	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
1678
1679	`YAKE Keyphrase Relationships:`
1680	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
1681
1682	`Timing Comparison:`
1683	`RAKE: 0.0000sec`
1684	`YAKE: 0.0000sec`
1685	`KeyBERT: 0.0105sec`
1686	`Dependencies: 0.0057sec`
1687	`Fastest: RAKE`
1688
1689	`================================================================================`
1690	`Message 11:`
1691	`RASCOE: And it seemed like what she was saying was stay grounded. Like, yeah, aspire to the biggest things, but stay grounded. Do you feel like Kanye, I mean, he's - he was searching for fame. Do you feel like fame was what he thought it would be?`
1692	`--------------------------------------------------------------------------------`
1693	`RAKE Keywords:`
1694	`- feel like kanye (score: 8.25) → feel like Kanye`
1695	`- feel like fame (score: 7.25)`
1696	`- seemed like (score: 4.25) → seem like`
1697	`- stay grounded (score: 4.00)`
1698	`- stay grounded (score: 4.00)`
1699	`- biggest things (score: 4.00) → big thing`
1700	`- like (score: 2.25)`
1701	`- fame (score: 2.00)`
1702	`- yeah (score: 1.00)`
1703	`- would (score: 1.00)`
1704	`- thought (score: 1.00) → think`
1705	`- searching (score: 1.00) → search`
1706	`- saying (score: 1.00) → say`
1707	`- rascoe (score: 1.00) → RASCOE`
1708	`- mean (score: 1.00)`
1709	`- aspire (score: 1.00)`
1710	`Total keywords: 16 extracted in 0.0000 seconds`
1711
1712	`YAKE Keywords:`
1713	`- stay grounded (score: 0.03)`
1714	`- RASCOE (score: 0.05) → RASCOE`
1715	`- grounded (score: 0.09)`
1716	`- feel like Kanye (score: 0.13) → feel like Kanye`
1717	`- stay (score: 0.15)`
1718	`- feel (score: 0.21)`
1719	`- biggest things (score: 0.23) → big thing`
1720	`- Kanye (score: 0.27) → Kanye`
1721	`- fame (score: 0.29)`
1722	`- yeah (score: 0.29)`
1723	`- aspire (score: 0.39)`
1724	`- things (score: 0.39) → thing`
1725	`- biggest (score: 0.48) → big`
1726	`- searching (score: 0.57) → search`
1727	`- feel like fame (score: 0.58)`
1728	`- thought (score: 0.62) → think`
1729	`- searching for fame (score: 0.84) → search for fame`
1730	`Total keywords: 17 extracted in 0.0105 seconds`
1731
1732	`KeyBERT Keywords:`
1733	`- kanye mean (score: 0.55)`
1734	`- kanye mean searching (score: 0.53)`
1735	`- like kanye mean (score: 0.52)`
1736	`- fame feel like (score: 0.52)`
1737	`- fame feel (score: 0.50)`
1738	`- feel like fame (score: 0.50)`
1739	`- fame thought (score: 0.49)`
1740	`- kanye (score: 0.49)`
1741	`- like kanye (score: 0.48)`
1742	`- like fame thought (score: 0.47)`
1743	`- feel like kanye (score: 0.46)`
1744	`- searching fame feel (score: 0.43)`
1745	`- like fame (score: 0.42)`
1746	`- fame (score: 0.41)`
1747	`- rascoe like saying (score: 0.38)`
1748	`- mean searching fame (score: 0.37)`
1749	`- saying stay grounded (score: 0.36)`
1750	`- searching fame (score: 0.35)`
1751	`- grounded like yeah (score: 0.32)`
1752	`- grounded like (score: 0.32)`
1753	`Total keywords: 20 extracted in 0.0408 seconds`
1754
1755	`Dependency Relations (extracted in 0.0110sec):`
1756
1757	`Sentence 1: RASCOE:`
1758	`RASCOE (PROPN) --[ROOT]--> RASCOE (PROPN)`
1759	`: (PUNCT) --[punct]--> RASCOE (PROPN)`
1760
1761	`Sentence 2: And it seemed like what she was saying was stay grounded.`
1762	`And (CCONJ) --[cc]--> seemed (VERB)`
1763	`it (PRON) --[nsubj]--> seemed (VERB)`
1764	`seemed (VERB) --[ROOT]--> seemed (VERB)`
1765	`like (ADP) --[prep]--> seemed (VERB)`
1766	`what (PRON) --[dobj]--> saying (VERB)`
1767	`she (PRON) --[nsubj]--> saying (VERB)`
1768	`was (AUX) --[aux]--> saying (VERB)`
1769	`saying (VERB) --[csubj]--> was (AUX)`
1770	`was (AUX) --[pcomp]--> like (ADP)`
1771	`stay (VERB) --[xcomp]--> was (AUX)`
1772	`grounded (ADJ) --[acomp]--> was (AUX)`
1773	`. (PUNCT) --[punct]--> seemed (VERB)`
1774
1775	`Sentence 3: Like, yeah, aspire to the biggest things, but stay grounded.`
1776	`Like (INTJ) --[intj]--> aspire (NOUN)`
1777	`, (PUNCT) --[punct]--> Like (INTJ)`
1778	`yeah (INTJ) --[intj]--> Like (INTJ)`
1779	`, (PUNCT) --[punct]--> aspire (NOUN)`
1780	`aspire (NOUN) --[ROOT]--> aspire (NOUN)`
1781	`to (ADP) --[prep]--> aspire (NOUN)`
1782	`the (DET) --[det]--> things (NOUN)`
1783	`biggest (ADJ) --[amod]--> things (NOUN)`
1784	`things (NOUN) --[pobj]--> to (ADP)`
1785	`, (PUNCT) --[punct]--> aspire (NOUN)`
1786	`but (CCONJ) --[cc]--> aspire (NOUN)`
1787	`stay (VERB) --[conj]--> aspire (NOUN)`
1788	`grounded (ADJ) --[acomp]--> stay (VERB)`
1789	`. (PUNCT) --[punct]--> aspire (NOUN)`
1790
1791	`Sentence 4: Do you feel like Kanye, I mean, he's - he was searching for fame.`
1792	`Do (AUX) --[aux]--> feel (VERB)`
1793	`you (PRON) --[nsubj]--> feel (VERB)`
1794	`feel (VERB) --[ROOT]--> feel (VERB)`
1795	`like (ADP) --[prep]--> feel (VERB)`
1796	`Kanye (PROPN) --[pobj]--> like (ADP)`
1797	`, (PUNCT) --[punct]--> 's (AUX)`
1798	`I (PRON) --[nsubj]--> mean (VERB)`
1799	`mean (VERB) --[parataxis]--> 's (AUX)`
1800	`, (PUNCT) --[punct]--> mean (VERB)`
1801	`he (PRON) --[nsubj]--> 's (AUX)`
1802	`'s (AUX) --[parataxis]--> feel (VERB)`
1803	`- (PUNCT) --[punct]--> searching (VERB)`
1804	`he (PRON) --[nsubj]--> searching (VERB)`
1805	`was (AUX) --[aux]--> searching (VERB)`
1806	`searching (VERB) --[ccomp]--> feel (VERB)`
1807	`for (ADP) --[prep]--> searching (VERB)`
1808	`fame (NOUN) --[pobj]--> for (ADP)`
1809	`. (PUNCT) --[punct]--> searching (VERB)`
1810
1811	`Sentence 5: Do you feel like fame was what he thought it would be?`
1812	`Do (AUX) --[aux]--> feel (VERB)`
1813	`you (PRON) --[nsubj]--> feel (VERB)`
1814	`feel (VERB) --[ROOT]--> feel (VERB)`
1815	`like (SCONJ) --[mark]--> was (AUX)`
1816	`fame (NOUN) --[nsubj]--> was (AUX)`
1817	`was (AUX) --[advcl]--> feel (VERB)`
1818	`what (PRON) --[dobj]--> be (AUX)`
1819	`he (PRON) --[nsubj]--> thought (VERB)`
1820	`thought (VERB) --[ccomp]--> was (AUX)`
1821	`it (PRON) --[nsubj]--> be (AUX)`
1822	`would (AUX) --[aux]--> be (AUX)`
1823	`be (AUX) --[ccomp]--> thought (VERB)`
1824	`? (PUNCT) --[punct]--> feel (VERB)`
1825
1826	`Total sentences: 5`
1827
1828	`RAKE Keyphrase Relationships:`
1829	`noun phrase: "Like, yeah, aspire" contains [like], [yeah], [aspire]`
1830	`verb phrase: "seemed like" contains [seemed like], [like]`
1831	`Total relationships found: 2`
1832
1833	`YAKE Keyphrase Relationships:`
1834	`noun phrase: "Like, yeah, aspire" contains [yeah], [aspire]`
1835	`noun phrase: "the biggest things" contains [biggest things], [things], [biggest]`
1836	`Total relationships found: 2`
1837
1838	`Timing Comparison:`
1839	`RAKE: 0.0000sec`
1840	`YAKE: 0.0105sec`
1841	`KeyBERT: 0.0408sec`
1842	`Dependencies: 0.0110sec`
1843	`Fastest: RAKE`
1844
1845	`================================================================================`
1846	`Message 12:`
1847	`MARTIN: Hopeful. Yeah. Yeah. That's funny, isn't it? How did it come to you? Was it - did the sad part come to you first or the hopeful part come to you first?`
1848	`--------------------------------------------------------------------------------`
1849	`RAKE Keywords:`
1850	`- sad part come (score: 8.33)`
1851	`- hopeful part come (score: 7.33)`
1852	`- come (score: 2.33)`
1853	`- hopeful (score: 2.00) → Hopeful`
1854	`- yeah (score: 1.00)`
1855	`- yeah (score: 1.00)`
1856	`- martin (score: 1.00) → MARTIN`
1857	`- funny (score: 1.00)`
1858	`- first (score: 1.00)`
1859	`- first (score: 1.00)`
1860	`Total keywords: 10 extracted in 0.0000 seconds`
1861
1862	`YAKE Keywords:`
1863	`- MARTIN (score: 0.05) → MARTIN`
1864	`- Yeah (score: 0.12)`
1865	`- Hopeful (score: 0.26) → Hopeful`
1866	`- part (score: 0.36)`
1867	`- hopeful part (score: 0.59)`
1868	`- funny (score: 0.61)`
1869	`- sad (score: 0.75)`
1870	`- sad part (score: 1.17)`
1871	`Total keywords: 8 extracted in 0.0035 seconds`
1872
1873	`KeyBERT Keywords:`
1874	`- martin hopeful (score: 0.62)`
1875	`- martin hopeful yeah (score: 0.60)`
1876	`- sad come hopeful (score: 0.57)`
1877	`- come hopeful (score: 0.54)`
1878	`- come hopeful come (score: 0.51)`
1879	`- did sad come (score: 0.50)`
1880	`- hopeful (score: 0.46)`
1881	`- hopeful come (score: 0.43)`
1882	`- martin (score: 0.43)`
1883	`- hopeful yeah (score: 0.39)`
1884	`- hopeful yeah yeah (score: 0.39)`
1885	`- did sad (score: 0.39)`
1886	`- come did sad (score: 0.34)`
1887	`- sad come (score: 0.29)`
1888	`- sad (score: 0.26)`
1889	`- did come did (score: 0.18)`
1890	`- did come (score: 0.17)`
1891	`- yeah yeah funny (score: 0.17)`
1892	`- yeah funny (score: 0.17)`
1893	`- funny isn (score: 0.17)`
1894	`Total keywords: 20 extracted in 0.0086 seconds`
1895
1896	`Dependency Relations (extracted in 0.0177sec):`
1897
1898	`Sentence 1: MARTIN: Hopeful.`
1899	`MARTIN (PROPN) --[ROOT]--> MARTIN (PROPN)`
1900	`: (PUNCT) --[punct]--> MARTIN (PROPN)`
1901	`Hopeful (PROPN) --[appos]--> MARTIN (PROPN)`
1902	`. (PUNCT) --[punct]--> MARTIN (PROPN)`
1903
1904	`Sentence 2: Yeah.`
1905	`Yeah (INTJ) --[ROOT]--> Yeah (INTJ)`
1906	`. (PUNCT) --[punct]--> Yeah (INTJ)`
1907
1908	`Sentence 3: Yeah.`
1909	`Yeah (INTJ) --[ROOT]--> Yeah (INTJ)`
1910	`. (PUNCT) --[punct]--> Yeah (INTJ)`
1911
1912	`Sentence 4: That's funny, isn't it?`
1913	`That (PRON) --[nsubj]--> 's (AUX)`
1914	`'s (AUX) --[ccomp]--> is (AUX)`
1915	`funny (ADJ) --[acomp]--> 's (AUX)`
1916	`, (PUNCT) --[punct]--> is (AUX)`
1917	`is (AUX) --[ROOT]--> is (AUX)`
1918	`n't (PART) --[neg]--> is (AUX)`
1919	`it (PRON) --[nsubj]--> is (AUX)`
1920	`? (PUNCT) --[punct]--> is (AUX)`
1921
1922	`Sentence 5: How did it come to you?`
1923	`How (SCONJ) --[advmod]--> come (VERB)`
1924	`did (AUX) --[aux]--> come (VERB)`
1925	`it (PRON) --[nsubj]--> come (VERB)`
1926	`come (VERB) --[ROOT]--> come (VERB)`
1927	`to (ADP) --[prep]--> come (VERB)`
1928	`you (PRON) --[pobj]--> to (ADP)`
1929	`? (PUNCT) --[punct]--> come (VERB)`
1930
1931	`Sentence 6: Was it - did the sad part come to you first or the hopeful part come to you first?`
1932	`Was (AUX) --[auxpass]--> come (VERB)`
1933	`it (PRON) --[nsubj]--> Was (AUX)`
1934	`- (PUNCT) --[punct]--> come (VERB)`
1935	`did (AUX) --[aux]--> come (VERB)`
1936	`the (DET) --[det]--> part (NOUN)`
1937	`sad (ADJ) --[amod]--> part (NOUN)`
1938	`part (NOUN) --[nsubj]--> come (VERB)`
1939	`come (VERB) --[ROOT]--> come (VERB)`
1940	`to (ADP) --[prep]--> come (VERB)`
1941	`you (PRON) --[pobj]--> to (ADP)`
1942	`first (ADV) --[advmod]--> come (VERB)`
1943	`or (CCONJ) --[cc]--> come (VERB)`
1944	`the (DET) --[det]--> part (NOUN)`
1945	`hopeful (ADJ) --[amod]--> part (NOUN)`
1946	`part (NOUN) --[nsubj]--> come (VERB)`
1947	`come (VERB) --[conj]--> come (VERB)`
1948	`to (ADP) --[prep]--> come (VERB)`
1949	`you (PRON) --[pobj]--> to (ADP)`
1950	`first (ADV) --[advmod]--> come (VERB)`
1951	`? (PUNCT) --[punct]--> come (VERB)`
1952
1953	`Total sentences: 6`
1954
1955	`RAKE Keyphrase Relationships:`
1956	`verb phrase: "come Was did to first" contains [come], [first], [first]`
1957	`verb phrase: "come to first" contains [come], [first], [first]`
1958	`Total relationships found: 2`
1959
1960	`YAKE Keyphrase Relationships:`
1961	`noun phrase: "the sad part" contains [part], [sad], [sad part]`
1962	`noun phrase: "the hopeful part" contains [hopeful], [part], [hopeful part]`
1963	`Total relationships found: 2`
1964
1965	`Timing Comparison:`
1966	`RAKE: 0.0000sec`
1967	`YAKE: 0.0035sec`
1968	`KeyBERT: 0.0086sec`
1969	`Dependencies: 0.0177sec`
1970	`Fastest: RAKE`
1971
1972	`================================================================================`
1973	`Message 13:`
1974	`CORNISH: And I'm still just like, wait - so you just moved? They had to get college degrees again. So it's really astounding, and I can't take credit, in a way, for anything that I've done because I just feel like it all comes from them. They don't take no for an answer. There's no room they think they can't or shouldn't be in. Even if people don't treat them well, they are - they always hold their heads up high.`
1975	`--------------------------------------------------------------------------------`
1976	`RAKE Keywords:`
1977	`- get college degrees (score: 9.00) → get college degree`
1978	`- really astounding (score: 4.00)`
1979	`- always hold (score: 4.00)`
1980	`- take credit (score: 3.50)`
1981	`- feel like (score: 3.50)`
1982	`- take (score: 1.50)`
1983	`- like (score: 1.50)`
1984	`- well (score: 1.00)`
1985	`- way (score: 1.00)`
1986	`- wait (score: 1.00)`
1987	`- treat (score: 1.00)`
1988	`- think (score: 1.00)`
1989	`- still (score: 1.00)`
1990	`- room (score: 1.00)`
1991	`- people (score: 1.00)`
1992	`- moved (score: 1.00) → move`
1993	`- high (score: 1.00)`
1994	`- heads (score: 1.00) → head`
1995	`- even (score: 1.00)`
1996	`- done (score: 1.00) → do`
1997	`- cornish (score: 1.00) → CORNISH`
1998	`- comes (score: 1.00) → come`
1999	`- anything (score: 1.00)`
2000	`- answer (score: 1.00)`
2001	`Total keywords: 24 extracted in 0.0000 seconds`
2002
2003	`YAKE Keywords:`
2004	`- CORNISH (score: 0.04) → CORNISH`
2005	`- wait (score: 0.07)`
2006	`- moved (score: 0.10) → move`
2007	`- college degrees (score: 0.12) → college degree`
2008	`- heads up high (score: 0.32) → head up high`
2009	`- college (score: 0.33)`
2010	`- degrees (score: 0.33) → degree`
2011	`- astounding (score: 0.36)`
2012	`- credit (score: 0.36)`
2013	`- hold their heads (score: 0.39) → hold their head`
2014	`- answer (score: 0.40)`
2015	`- feel (score: 0.42)`
2016	`- high (score: 0.46)`
2017	`- room (score: 0.50)`
2018	`- people (score: 0.53)`
2019	`- treat (score: 0.53)`
2020	`- hold (score: 0.53)`
2021	`- heads (score: 0.53) → head`
2022	`Total keywords: 18 extracted in 0.0072 seconds`
2023
2024	`KeyBERT Keywords:`
2025	`- cornish just like (score: 0.40)`
2026	`- cornish just (score: 0.38)`
2027	`- moved college degrees (score: 0.38)`
2028	`- cornish (score: 0.38)`
2029	`- people don treat (score: 0.37)`
2030	`- moved college (score: 0.36)`
2031	`- just moved college (score: 0.34)`
2032	`- college degrees really (score: 0.28)`
2033	`- degrees really astounding (score: 0.26)`
2034	`- college degrees (score: 0.23)`
2035	`- don treat (score: 0.23)`
2036	`- just feel (score: 0.21)`
2037	`- college (score: 0.20)`
2038	`- just feel like (score: 0.19)`
2039	`- moved (score: 0.18)`
2040	`- just like (score: 0.18)`
2041	`- people don (score: 0.17)`
2042	`- degrees really (score: 0.17)`
2043	`- ve just feel (score: 0.17)`
2044	`- feel (score: 0.16)`
2045	`Total keywords: 20 extracted in 0.0479 seconds`
2046
2047	`Dependency Relations (extracted in 0.0192sec):`
2048
2049	`Sentence 1: CORNISH:`
2050	`CORNISH (VERB) --[ROOT]--> CORNISH (VERB)`
2051	`: (PUNCT) --[punct]--> CORNISH (VERB)`
2052
2053	`Sentence 2: And I'm still just like, wait - so you just moved?`
2054	`And (CCONJ) --[cc]--> 'm (AUX)`
2055	`I (PRON) --[nsubj]--> 'm (AUX)`
2056	`'m (AUX) --[ccomp]--> moved (VERB)`
2057	`still (ADV) --[advmod]--> 'm (AUX)`
2058	`just (ADV) --[advmod]--> like (INTJ)`
2059	`like (INTJ) --[advmod]--> 'm (AUX)`
2060	`, (PUNCT) --[punct]--> 'm (AUX)`
2061	`wait (VERB) --[dep]--> 'm (AUX)`
2062	`- (PUNCT) --[punct]--> moved (VERB)`
2063	`so (ADV) --[advmod]--> moved (VERB)`
2064	`you (PRON) --[nsubj]--> moved (VERB)`
2065	`just (ADV) --[advmod]--> moved (VERB)`
2066	`moved (VERB) --[ROOT]--> moved (VERB)`
2067	`? (PUNCT) --[punct]--> moved (VERB)`
2068
2069	`Sentence 3: They had to get college degrees again.`
2070	`They (PRON) --[nsubj]--> had (VERB)`
2071	`had (VERB) --[ROOT]--> had (VERB)`
2072	`to (PART) --[aux]--> get (VERB)`
2073	`get (VERB) --[xcomp]--> had (VERB)`
2074	`college (NOUN) --[compound]--> degrees (NOUN)`
2075	`degrees (NOUN) --[dobj]--> get (VERB)`
2076	`again (ADV) --[advmod]--> get (VERB)`
2077	`. (PUNCT) --[punct]--> had (VERB)`
2078
2079	`Sentence 4: So it's really astounding, and I can't take credit, in a way, for anything that I've done because I just feel like it all comes from them.`
2080	`So (ADV) --[advmod]--> 's (AUX)`
2081	`it (PRON) --[nsubj]--> 's (AUX)`
2082	`'s (AUX) --[ROOT]--> 's (AUX)`
2083	`really (ADV) --[advmod]--> astounding (ADJ)`
2084	`astounding (ADJ) --[acomp]--> 's (AUX)`
2085	`, (PUNCT) --[punct]--> 's (AUX)`
2086	`and (CCONJ) --[cc]--> 's (AUX)`
2087	`I (PRON) --[nsubj]--> take (VERB)`
2088	`ca (AUX) --[aux]--> take (VERB)`
2089	`n't (PART) --[neg]--> take (VERB)`
2090	`take (VERB) --[conj]--> 's (AUX)`
2091	`credit (NOUN) --[dobj]--> take (VERB)`
2092	`, (PUNCT) --[punct]--> take (VERB)`
2093	`in (ADP) --[prep]--> take (VERB)`
2094	`a (DET) --[det]--> way (NOUN)`
2095	`way (NOUN) --[pobj]--> in (ADP)`
2096	`, (PUNCT) --[punct]--> take (VERB)`
2097	`for (ADP) --[prep]--> take (VERB)`
2098	`anything (PRON) --[pobj]--> for (ADP)`
2099	`that (PRON) --[dobj]--> done (VERB)`
2100	`I (PRON) --[nsubj]--> done (VERB)`
2101	`'ve (AUX) --[aux]--> done (VERB)`
2102	`done (VERB) --[relcl]--> anything (PRON)`
2103	`because (SCONJ) --[mark]--> feel (VERB)`
2104	`I (PRON) --[nsubj]--> feel (VERB)`
2105	`just (ADV) --[advmod]--> feel (VERB)`
2106	`feel (VERB) --[advcl]--> take (VERB)`
2107	`like (SCONJ) --[mark]--> comes (VERB)`
2108	`it (PRON) --[nsubj]--> comes (VERB)`
2109	`all (PRON) --[appos]--> it (PRON)`
2110	`comes (VERB) --[advcl]--> feel (VERB)`
2111	`from (ADP) --[prep]--> comes (VERB)`
2112	`them (PRON) --[pobj]--> from (ADP)`
2113	`. (PUNCT) --[punct]--> take (VERB)`
2114
2115	`Sentence 5: They don't take no for an answer.`
2116	`They (PRON) --[nsubj]--> take (VERB)`
2117	`do (AUX) --[aux]--> take (VERB)`
2118	`n't (PART) --[neg]--> take (VERB)`
2119	`take (VERB) --[ROOT]--> take (VERB)`
2120	`no (INTJ) --[dobj]--> take (VERB)`
2121	`for (ADP) --[prep]--> take (VERB)`
2122	`an (DET) --[det]--> answer (NOUN)`
2123	`answer (NOUN) --[pobj]--> for (ADP)`
2124	`. (PUNCT) --[punct]--> take (VERB)`
2125
2126	`Sentence 6: There's no room they think they can't or shouldn't be in.`
2127	`There (PRON) --[expl]--> 's (VERB)`
2128	`'s (VERB) --[ROOT]--> 's (VERB)`
2129	`no (DET) --[det]--> room (NOUN)`
2130	`room (NOUN) --[attr]--> 's (VERB)`
2131	`they (PRON) --[nsubj]--> think (VERB)`
2132	`think (VERB) --[relcl]--> room (NOUN)`
2133	`they (PRON) --[nsubj]--> ca (AUX)`
2134	`ca (AUX) --[ccomp]--> think (VERB)`
2135	`n't (PART) --[neg]--> ca (AUX)`
2136	`or (CCONJ) --[cc]--> think (VERB)`
2137	`should (AUX) --[aux]--> be (AUX)`
2138	`n't (PART) --[neg]--> be (AUX)`
2139	`be (AUX) --[conj]--> think (VERB)`
2140	`in (ADP) --[prep]--> be (AUX)`
2141	`. (PUNCT) --[punct]--> 's (VERB)`
2142
2143	`Sentence 7: Even if people don't treat them well, they are - they always hold their heads up high.`
2144	`Even (ADV) --[advmod]--> treat (VERB)`
2145	`if (SCONJ) --[mark]--> treat (VERB)`
2146	`people (NOUN) --[nsubj]--> treat (VERB)`
2147	`do (AUX) --[aux]--> treat (VERB)`
2148	`n't (PART) --[neg]--> treat (VERB)`
2149	`treat (VERB) --[advcl]--> are (AUX)`
2150	`them (PRON) --[dobj]--> treat (VERB)`
2151	`well (ADV) --[advmod]--> treat (VERB)`
2152	`, (PUNCT) --[punct]--> are (AUX)`
2153	`they (PRON) --[nsubj]--> are (AUX)`
2154	`are (AUX) --[ccomp]--> hold (VERB)`
2155	`- (PUNCT) --[punct]--> hold (VERB)`
2156	`they (PRON) --[nsubj]--> hold (VERB)`
2157	`always (ADV) --[advmod]--> hold (VERB)`
2158	`hold (VERB) --[ROOT]--> hold (VERB)`
2159	`their (PRON) --[poss]--> heads (NOUN)`
2160	`heads (NOUN) --[dobj]--> hold (VERB)`
2161	`up (ADV) --[prt]--> hold (VERB)`
2162	`high (ADV) --[advmod]--> hold (VERB)`
2163	`. (PUNCT) --[punct]--> hold (VERB)`
2164
2165	`Total sentences: 7`
2166
2167	`RAKE Keyphrase Relationships:`
2168	`verb phrase: "treat Even do n't them well" contains [well], [treat], [even]`
2169	`verb phrase: "hold always heads high" contains [way], [high], [heads]`
2170	`Total relationships found: 2`
2171
2172	`YAKE Keyphrase Relationships:`
2173	`noun phrase: "college degrees" contains [college degrees], [college], [degrees]`
2174	`verb phrase: "hold always heads high" contains [high], [hold], [heads]`
2175	`Total relationships found: 2`
2176
2177	`Timing Comparison:`
2178	`RAKE: 0.0000sec`
2179	`YAKE: 0.0072sec`
2180	`KeyBERT: 0.0479sec`
2181	`Dependencies: 0.0192sec`
2182	`Fastest: RAKE`
2183
2184	`================================================================================`
2185	`Message 14:`
2186	`MCEVERS: Right.`
2187	`--------------------------------------------------------------------------------`
2188	`RAKE Keywords:`
2189	`- right (score: 1.00)`
2190	`- mcevers (score: 1.00) → mcever`
2191	`Total keywords: 2 extracted in 0.0000 seconds`
2192
2193	`YAKE Keywords:`
2194	`- MCEVERS (score: 0.03) → mcever`
2195	`Total keywords: 1 extracted in 0.0000 seconds`
2196
2197	`KeyBERT Keywords:`
2198	`- mcevers right (score: 0.90)`
2199	`- mcevers (score: 0.86)`
2200	`- right (score: 0.34)`
2201	`Total keywords: 3 extracted in 0.0215 seconds`
2202
2203	`Dependency Relations (extracted in 0.0104sec):`
2204
2205	`Sentence 1: MCEVERS:`
2206	`MCEVERS (NOUN) --[ROOT]--> MCEVERS (NOUN)`
2207	`: (PUNCT) --[punct]--> MCEVERS (NOUN)`
2208
2209	`Sentence 2: Right.`
2210	`Right (INTJ) --[ROOT]--> Right (INTJ)`
2211	`. (PUNCT) --[punct]--> Right (INTJ)`
2212
2213	`Total sentences: 2`
2214
2215	`RAKE Keyphrase Relationships:`
2216	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
2217
2218	`YAKE Keyphrase Relationships:`
2219	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
2220
2221	`Timing Comparison:`
2222	`RAKE: 0.0000sec`
2223	`YAKE: 0.0000sec`
2224	`KeyBERT: 0.0215sec`
2225	`Dependencies: 0.0104sec`
2226	`Fastest: RAKE`
2227
2228	`================================================================================`
2229	`Message 15:`
2230	`PHILLIPS: Yeah, yeah, for sure a bit because I didn't know him, I'll be very honest. And when I went back to the car and my wife said, are you sure he plays? And I was like, hey, either way, we're going to give him a ride. And that was it.`
2231	`--------------------------------------------------------------------------------`
2232	`RAKE Keywords:`
2233	`- wife said (score: 4.00) → wife say`
2234	`- went back (score: 4.00) → go back`
2235	`- either way (score: 4.00)`
2236	`- yeah (score: 1.00)`
2237	`- yeah (score: 1.00)`
2238	`- sure (score: 1.00)`
2239	`- sure (score: 1.00)`
2240	`- ride (score: 1.00)`
2241	`- plays (score: 1.00) → play`
2242	`- phillips (score: 1.00)`
2243	`- like (score: 1.00)`
2244	`- know (score: 1.00)`
2245	`- honest (score: 1.00)`
2246	`- hey (score: 1.00)`
2247	`- going (score: 1.00) → go`
2248	`- give (score: 1.00)`
2249	`- car (score: 1.00)`
2250	`- bit (score: 1.00)`
2251	`Total keywords: 18 extracted in 0.0000 seconds`
2252
2253	`YAKE Keywords:`
2254	`- Yeah (score: 0.02)`
2255	`- PHILLIPS (score: 0.05)`
2256	`- honest (score: 0.13)`
2257	`- bit (score: 0.18)`
2258	`- hey (score: 0.33)`
2259	`- plays (score: 0.34) → play`
2260	`- back (score: 0.43)`
2261	`- car (score: 0.43)`
2262	`- wife (score: 0.43)`
2263	`- ride (score: 0.43)`
2264	`- give (score: 0.52)`
2265	`Total keywords: 11 extracted in 0.0037 seconds`
2266
2267	`KeyBERT Keywords:`
2268	`- phillips yeah yeah (score: 0.55)`
2269	`- phillips yeah (score: 0.53)`
2270	`- phillips (score: 0.48)`
2271	`- plays like hey (score: 0.39)`
2272	`- sure plays like (score: 0.38)`
2273	`- said sure plays (score: 0.37)`
2274	`- plays (score: 0.36)`
2275	`- plays like (score: 0.35)`
2276	`- sure plays (score: 0.34)`
2277	`- honest went car (score: 0.29)`
2278	`- car wife said (score: 0.27)`
2279	`- ride (score: 0.25)`
2280	`- went car wife (score: 0.24)`
2281	`- going ride (score: 0.23)`
2282	`- wife said sure (score: 0.22)`
2283	`- car wife (score: 0.21)`
2284	`- went car (score: 0.21)`
2285	`- way going ride (score: 0.21)`
2286	`- wife said (score: 0.18)`
2287	`- ll honest went (score: 0.17)`
2288	`Total keywords: 20 extracted in 0.0339 seconds`
2289
2290	`Dependency Relations (extracted in 0.0135sec):`
2291
2292	`Sentence 1: PHILLIPS:`
2293	`PHILLIPS (NOUN) --[ROOT]--> PHILLIPS (NOUN)`
2294	`: (PUNCT) --[punct]--> PHILLIPS (NOUN)`
2295
2296	`Sentence 2: Yeah, yeah, for sure a bit because I didn't know him, I'll be very honest.`
2297	`Yeah (INTJ) --[intj]--> be (AUX)`
2298	`, (PUNCT) --[punct]--> Yeah (INTJ)`
2299	`yeah (INTJ) --[intj]--> Yeah (INTJ)`
2300	`, (PUNCT) --[punct]--> Yeah (INTJ)`
2301	`for (ADP) --[prep]--> Yeah (INTJ)`
2302	`sure (ADJ) --[amod]--> for (ADP)`
2303	`a (DET) --[det]--> bit (NOUN)`
2304	`bit (NOUN) --[npadvmod]--> Yeah (INTJ)`
2305	`because (SCONJ) --[mark]--> know (VERB)`
2306	`I (PRON) --[nsubj]--> know (VERB)`
2307	`did (AUX) --[aux]--> know (VERB)`
2308	`n't (PART) --[neg]--> know (VERB)`
2309	`know (VERB) --[advcl]--> be (AUX)`
2310	`him (PRON) --[dobj]--> know (VERB)`
2311	`, (PUNCT) --[punct]--> be (AUX)`
2312	`I (PRON) --[nsubj]--> be (AUX)`
2313	`'ll (AUX) --[aux]--> be (AUX)`
2314	`be (AUX) --[ROOT]--> be (AUX)`
2315	`very (ADV) --[advmod]--> honest (ADJ)`
2316	`honest (ADJ) --[acomp]--> be (AUX)`
2317	`. (PUNCT) --[punct]--> be (AUX)`
2318
2319	`Sentence 3: And when I went back to the car and my wife said, are you sure he plays?`
2320	`And (CCONJ) --[ROOT]--> And (CCONJ)`
2321	`when (SCONJ) --[advmod]--> went (VERB)`
2322	`I (PRON) --[nsubj]--> went (VERB)`
2323	`went (VERB) --[advcl]--> And (CCONJ)`
2324	`back (ADV) --[advmod]--> went (VERB)`
2325	`to (ADP) --[prep]--> back (ADV)`
2326	`the (DET) --[det]--> car (NOUN)`
2327	`car (NOUN) --[pobj]--> to (ADP)`
2328	`and (CCONJ) --[cc]--> went (VERB)`
2329	`my (PRON) --[poss]--> wife (NOUN)`
2330	`wife (NOUN) --[nsubj]--> said (VERB)`
2331	`said (VERB) --[conj]--> went (VERB)`
2332	`, (PUNCT) --[punct]--> said (VERB)`
2333	`are (AUX) --[ccomp]--> said (VERB)`
2334	`you (PRON) --[nsubj]--> are (AUX)`
2335	`sure (ADJ) --[acomp]--> are (AUX)`
2336	`he (PRON) --[nsubj]--> plays (VERB)`
2337	`plays (VERB) --[ccomp]--> sure (ADJ)`
2338	`? (PUNCT) --[punct]--> said (VERB)`
2339
2340	`Sentence 4: And I was like, hey, either way, we're going to give him a ride.`
2341	`And (CCONJ) --[cc]--> was (AUX)`
2342	`I (PRON) --[nsubj]--> was (AUX)`
2343	`was (AUX) --[ROOT]--> was (AUX)`
2344	`like (INTJ) --[intj]--> was (AUX)`
2345	`, (PUNCT) --[punct]--> was (AUX)`
2346	`hey (INTJ) --[intj]--> was (AUX)`
2347	`, (PUNCT) --[punct]--> was (AUX)`
2348	`either (DET) --[det]--> way (NOUN)`
2349	`way (NOUN) --[npadvmod]--> going (VERB)`
2350	`, (PUNCT) --[punct]--> going (VERB)`
2351	`we (PRON) --[nsubj]--> going (VERB)`
2352	`'re (AUX) --[aux]--> going (VERB)`
2353	`going (VERB) --[advcl]--> was (AUX)`
2354	`to (PART) --[aux]--> give (VERB)`
2355	`give (VERB) --[xcomp]--> going (VERB)`
2356	`him (PRON) --[dative]--> give (VERB)`
2357	`a (DET) --[det]--> ride (NOUN)`
2358	`ride (NOUN) --[dobj]--> give (VERB)`
2359	`. (PUNCT) --[punct]--> was (AUX)`
2360
2361	`Sentence 5: And that was it.`
2362	`And (CCONJ) --[cc]--> was (AUX)`
2363	`that (PRON) --[nsubj]--> was (AUX)`
2364	`was (AUX) --[ROOT]--> was (AUX)`
2365	`it (PRON) --[attr]--> was (AUX)`
2366	`. (PUNCT) --[punct]--> was (AUX)`
2367
2368	`Total sentences: 5`
2369
2370	`RAKE Keyphrase Relationships:`
2371	`verb phrase: "give to ride" contains [ride], [give]`
2372	`Total relationships found: 1`
2373
2374	`YAKE Keyphrase Relationships:`
2375	`verb phrase: "give to ride" contains [ride], [give]`
2376	`Total relationships found: 1`
2377
2378	`Timing Comparison:`
2379	`RAKE: 0.0000sec`
2380	`YAKE: 0.0037sec`
2381	`KeyBERT: 0.0339sec`
2382	`Dependencies: 0.0135sec`
2383	`Fastest: RAKE`
2384
2385	`================================================================================`
2386	`Message 16:`
2387	HILL: There's fear among administrators of hospitals and nursing homes that there'll be staff shortages. Many hospitals, their vaccination rate is 90% - about that. But that's still a lot of workers to lose in one shot. So already, you're seeing some hospitals pare back services. You know, they're not taking in elective surgeries, for instance. One hospital is not doing maternity anymore. So Governor Hochul came by yesterday, and she said, I'm prepared to declare a state of emergency that would help fill any gap in staff shortages.
2388	`--------------------------------------------------------------------------------`
2389	`RAKE Keywords:`
2390	`- would help fill (score: 9.00)`
2391	`- governor hochul came (score: 9.00) → Governor Hochul come`
2392	`- fear among administrators (score: 9.00) → fear among administrator`
2393	`- vaccination rate (score: 4.00)`
2394	`- staff shortages (score: 4.00) → staff shortage`
2395	`- staff shortages (score: 4.00) → staff shortage`
2396	`- one shot (score: 4.00)`
2397	`- one hospital (score: 4.00)`
2398	`- nursing homes (score: 4.00) → nursing home`
2399	`- maternity anymore (score: 4.00)`
2400	`- elective surgeries (score: 4.00) → elective surgery`
2401	`- many hospitals (score: 3.50) → many hospital`
2402	`- hospitals (score: 1.50) → hospital`
2403	`- yesterday (score: 1.00)`
2404	`- workers (score: 1.00) → worker`
2405	`- taking (score: 1.00) → take`
2406	`- still (score: 1.00)`
2407	`- state (score: 1.00)`
2408	`- seeing (score: 1.00) → see`
2409	`- said (score: 1.00) → say`
2410	`- prepared (score: 1.00)`
2411	`- lot (score: 1.00)`
2412	`- lose (score: 1.00)`
2413	`- know (score: 1.00)`
2414	`- instance (score: 1.00)`
2415	`- hill (score: 1.00) → HILL`
2416	`- gap (score: 1.00)`
2417	`- emergency (score: 1.00)`
2418	`- declare (score: 1.00)`
2419	`- already (score: 1.00)`
2420	`- 90 (score: 1.00)`
2421	`Total keywords: 31 extracted in 0.0020 seconds`
2422
2423	`YAKE Keywords:`
2424	`- HILL (score: 0.05) → HILL`
2425	`- fear among administrators (score: 0.05) → fear among administrator`
2426	`- nursing homes (score: 0.05) → nursing home`
2427	`- staff shortages (score: 0.14) → staff shortage`
2428	`- shortages (score: 0.19) → shortage`
2429	`- hospitals (score: 0.20) → hospital`
2430	`- fear (score: 0.23)`
2431	`- administrators (score: 0.23) → administrator`
2432	`- nursing (score: 0.23)`
2433	`- homes (score: 0.23) → home`
2434	`- Governor Hochul (score: 0.24) → Governor Hochul`
2435	`- staff (score: 0.28)`
2436	`- vaccination rate (score: 0.34)`
2437	`- administrators of hospitals (score: 0.37) → administrator of hospital`
2438	`- hospitals and nursing (score: 0.37) → hospital and nursing`
2439	`- Governor (score: 0.44) → Governor`
2440	`- Hochul (score: 0.44) → Hochul`
2441	`- vaccination (score: 0.50)`
2442	`- rate (score: 0.50)`
2443	`- shot (score: 0.51)`
2444	`Total keywords: 20 extracted in 0.0212 seconds`
2445
2446	`KeyBERT Keywords:`
2447	`- staff shortages hospitals (score: 0.71)`
2448	`- shortages hospitals (score: 0.65)`
2449	`- shortages hospitals vaccination (score: 0.63)`
2450	`- staff shortages (score: 0.63)`
2451	`- state emergency (score: 0.53)`
2452	`- ll staff shortages (score: 0.53)`
2453	`- state emergency help (score: 0.52)`
2454	`- hospitals nursing homes (score: 0.50)`
2455	`- gap staff shortages (score: 0.50)`
2456	`- seeing hospitals pare (score: 0.48)`
2457	`- declare state emergency (score: 0.48)`
2458	`- hospitals pare services (score: 0.48)`
2459	`- emergency help gap (score: 0.47)`
2460	`- shortages (score: 0.47)`
2461	`- hospitals (score: 0.47)`
2462	`- seeing hospitals (score: 0.46)`
2463	`- hospitals vaccination (score: 0.46)`
2464	`- hospital doing (score: 0.44)`
2465	`- fear administrators hospitals (score: 0.44)`
2466	`- hospitals pare (score: 0.44)`
2467	`Total keywords: 20 extracted in 0.0814 seconds`
2468
2469	`Dependency Relations (extracted in 0.0218sec):`
2470
2471	`Sentence 1: HILL:`
2472	`HILL (PROPN) --[ROOT]--> HILL (PROPN)`
2473	`: (PUNCT) --[punct]--> HILL (PROPN)`
2474
2475	`Sentence 2: There's fear among administrators of hospitals and nursing homes that there'll be staff shortages.`
2476	`There (PRON) --[expl]--> 's (VERB)`
2477	`'s (VERB) --[ROOT]--> 's (VERB)`
2478	`fear (NOUN) --[attr]--> 's (VERB)`
2479	`among (ADP) --[prep]--> fear (NOUN)`
2480	`administrators (NOUN) --[pobj]--> among (ADP)`
2481	`of (ADP) --[prep]--> administrators (NOUN)`
2482	`hospitals (NOUN) --[pobj]--> of (ADP)`
2483	`and (CCONJ) --[cc]--> hospitals (NOUN)`
2484	`nursing (NOUN) --[compound]--> homes (NOUN)`
2485	`homes (NOUN) --[conj]--> hospitals (NOUN)`
2486	`that (PRON) --[mark]--> be (AUX)`
2487	`there (PRON) --[expl]--> be (AUX)`
2488	`'ll (AUX) --[aux]--> be (AUX)`
2489	`be (AUX) --[acl]--> fear (NOUN)`
2490	`staff (NOUN) --[compound]--> shortages (NOUN)`
2491	`shortages (NOUN) --[attr]--> be (AUX)`
2492	`. (PUNCT) --[punct]--> 's (VERB)`
2493
2494	`Sentence 3: Many hospitals, their vaccination rate is 90% - about that.`
2495	`Many (ADJ) --[amod]--> hospitals (NOUN)`
2496	`hospitals (NOUN) --[nsubj]--> is (AUX)`
2497	`, (PUNCT) --[punct]--> is (AUX)`
2498	`their (PRON) --[poss]--> rate (NOUN)`
2499	`vaccination (NOUN) --[compound]--> rate (NOUN)`
2500	`rate (NOUN) --[nsubj]--> is (AUX)`
2501	`is (AUX) --[ROOT]--> is (AUX)`
2502	`90 (NUM) --[nummod]--> % (NOUN)`
2503	`% (NOUN) --[attr]--> is (AUX)`
2504	`- (PUNCT) --[punct]--> % (NOUN)`
2505	`about (ADP) --[prep]--> % (NOUN)`
2506	`that (PRON) --[pobj]--> about (ADP)`
2507	`. (PUNCT) --[punct]--> is (AUX)`
2508
2509	`Sentence 4: But that's still a lot of workers to lose in one shot.`
2510	`But (CCONJ) --[cc]--> 's (AUX)`
2511	`that (PRON) --[nsubj]--> 's (AUX)`
2512	`'s (AUX) --[ROOT]--> 's (AUX)`
2513	`still (ADV) --[advmod]--> 's (AUX)`
2514	`a (DET) --[det]--> lot (NOUN)`
2515	`lot (NOUN) --[attr]--> 's (AUX)`
2516	`of (ADP) --[prep]--> lot (NOUN)`
2517	`workers (NOUN) --[pobj]--> of (ADP)`
2518	`to (PART) --[aux]--> lose (VERB)`
2519	`lose (VERB) --[relcl]--> lot (NOUN)`
2520	`in (ADP) --[prep]--> lose (VERB)`
2521	`one (NUM) --[nummod]--> shot (NOUN)`
2522	`shot (NOUN) --[pobj]--> in (ADP)`
2523	`. (PUNCT) --[punct]--> 's (AUX)`
2524
2525	`Sentence 5: So already, you're seeing some hospitals pare back services.`
2526	`So (ADV) --[advmod]--> seeing (VERB)`
2527	`already (ADV) --[advmod]--> seeing (VERB)`
2528	`, (PUNCT) --[punct]--> seeing (VERB)`
2529	`you (PRON) --[nsubj]--> seeing (VERB)`
2530	`'re (AUX) --[aux]--> seeing (VERB)`
2531	`seeing (VERB) --[ROOT]--> seeing (VERB)`
2532	`some (DET) --[det]--> hospitals (NOUN)`
2533	`hospitals (NOUN) --[nsubj]--> pare (VERB)`
2534	`pare (VERB) --[ccomp]--> seeing (VERB)`
2535	`back (ADP) --[amod]--> services (NOUN)`
2536	`services (NOUN) --[dobj]--> pare (VERB)`
2537	`. (PUNCT) --[punct]--> seeing (VERB)`
2538
2539	`Sentence 6: You know, they're not taking in elective surgeries, for instance.`
2540	`You (PRON) --[nsubj]--> know (VERB)`
2541	`know (VERB) --[parataxis]--> taking (VERB)`
2542	`, (PUNCT) --[punct]--> taking (VERB)`
2543	`they (PRON) --[nsubj]--> taking (VERB)`
2544	`'re (AUX) --[aux]--> taking (VERB)`
2545	`not (PART) --[neg]--> taking (VERB)`
2546	`taking (VERB) --[ROOT]--> taking (VERB)`
2547	`in (ADP) --[prt]--> taking (VERB)`
2548	`elective (ADJ) --[amod]--> surgeries (NOUN)`
2549	`surgeries (NOUN) --[dobj]--> taking (VERB)`
2550	`, (PUNCT) --[punct]--> taking (VERB)`
2551	`for (ADP) --[prep]--> taking (VERB)`
2552	`instance (NOUN) --[pobj]--> for (ADP)`
2553	`. (PUNCT) --[punct]--> taking (VERB)`
2554
2555	`Sentence 7: One hospital is not doing maternity anymore.`
2556	`One (NUM) --[nummod]--> hospital (NOUN)`
2557	`hospital (NOUN) --[nsubj]--> doing (VERB)`
2558	`is (AUX) --[aux]--> doing (VERB)`
2559	`not (PART) --[neg]--> doing (VERB)`
2560	`doing (VERB) --[ROOT]--> doing (VERB)`
2561	`maternity (NOUN) --[dobj]--> doing (VERB)`
2562	`anymore (ADV) --[advmod]--> doing (VERB)`
2563	`. (PUNCT) --[punct]--> doing (VERB)`
2564
2565	`Sentence 8: So Governor Hochul came by yesterday, and she said, I'm prepared to declare a state of emergency that would help fill any gap in staff shortages.`
2566	`So (ADV) --[advmod]--> came (VERB)`
2567	`Governor (PROPN) --[compound]--> Hochul (PROPN)`
2568	`Hochul (PROPN) --[nsubj]--> came (VERB)`
2569	`came (VERB) --[ROOT]--> came (VERB)`
2570	`by (ADP) --[prep]--> came (VERB)`
2571	`yesterday (NOUN) --[npadvmod]--> came (VERB)`
2572	`, (PUNCT) --[punct]--> came (VERB)`
2573	`and (CCONJ) --[cc]--> came (VERB)`
2574	`she (PRON) --[nsubj]--> said (VERB)`
2575	`said (VERB) --[conj]--> came (VERB)`
2576	`, (PUNCT) --[punct]--> said (VERB)`
2577	`I (PRON) --[nsubj]--> 'm (AUX)`
2578	`'m (AUX) --[ccomp]--> said (VERB)`
2579	`prepared (ADJ) --[acomp]--> 'm (AUX)`
2580	`to (PART) --[aux]--> declare (VERB)`
2581	`declare (VERB) --[xcomp]--> prepared (ADJ)`
2582	`a (DET) --[det]--> state (NOUN)`
2583	`state (NOUN) --[dobj]--> declare (VERB)`
2584	`of (ADP) --[prep]--> state (NOUN)`
2585	`emergency (NOUN) --[pobj]--> of (ADP)`
2586	`that (PRON) --[nsubj]--> help (VERB)`
2587	`would (AUX) --[aux]--> help (VERB)`
2588	`help (VERB) --[relcl]--> state (NOUN)`
2589	`fill (VERB) --[xcomp]--> help (VERB)`
2590	`any (DET) --[det]--> gap (NOUN)`
2591	`gap (NOUN) --[dobj]--> fill (VERB)`
2592	`in (ADP) --[prep]--> gap (NOUN)`
2593	`staff (NOUN) --[compound]--> shortages (NOUN)`
2594	`shortages (NOUN) --[pobj]--> in (ADP)`
2595	`. (PUNCT) --[punct]--> said (VERB)`
2596
2597	`Total sentences: 8`
2598
2599	`RAKE Keyphrase Relationships:`
2600	`noun phrase: "staff shortages" contains [staff shortages], [staff shortages]`
2601	`noun phrase: "Many hospitals" contains [many hospitals], [hospitals]`
2602	`noun phrase: "staff shortages" contains [staff shortages], [staff shortages]`
2603	`verb phrase: "seeing So already 're" contains [seeing], [already]`
2604	`verb phrase: "declare to state" contains [state], [declare]`
2605	`Total relationships found: 5`
2606
2607	`YAKE Keyphrase Relationships:`
2608	`noun phrase: "nursing homes" contains [nursing homes], [nursing], [homes]`
2609	`noun phrase: "staff shortages" contains [staff shortages], [shortages], [staff]`
2610	`noun phrase: "their vaccination rate" contains [vaccination rate], [vaccination], [rate]`
2611	`noun phrase: "Governor Hochul" contains [governor hochul], [governor], [hochul]`
2612	`noun phrase: "staff shortages" contains [staff shortages], [shortages], [staff]`
2613	`Total relationships found: 5`
2614
2615	`Timing Comparison:`
2616	`RAKE: 0.0020sec`
2617	`YAKE: 0.0212sec`
2618	`KeyBERT: 0.0814sec`
2619	`Dependencies: 0.0218sec`
2620	`Fastest: RAKE`
2621
2622	`================================================================================`
2623	`Message 17:`
2624	`KELLY: Oh, yes.`
2625	`--------------------------------------------------------------------------------`
2626	`RAKE Keywords:`
2627	`- yes (score: 1.00)`
2628	`- oh (score: 1.00)`
2629	`- kelly (score: 1.00) → KELLY`
2630	`Total keywords: 3 extracted in 0.0000 seconds`
2631
2632	`YAKE Keywords:`
2633	`- KELLY (score: 0.03) → KELLY`
2634	`Total keywords: 1 extracted in 0.0000 seconds`
2635
2636	`KeyBERT Keywords:`
2637	`- kelly oh yes (score: 0.77)`
2638	`- kelly oh (score: 0.69)`
2639	`- kelly (score: 0.61)`
2640	`- oh yes (score: 0.35)`
2641	`- yes (score: 0.27)`
2642	`- oh (score: 0.26)`
2643	`Total keywords: 6 extracted in 0.0115 seconds`
2644
2645	`Dependency Relations (extracted in 0.0000sec):`
2646
2647	`Sentence 1: KELLY:`
2648	`KELLY (PROPN) --[ROOT]--> KELLY (PROPN)`
2649	`: (PUNCT) --[punct]--> KELLY (PROPN)`
2650
2651	`Sentence 2: Oh, yes.`
2652	`Oh (INTJ) --[ROOT]--> Oh (INTJ)`
2653	`, (PUNCT) --[punct]--> Oh (INTJ)`
2654	`yes (INTJ) --[intj]--> Oh (INTJ)`
2655	`. (PUNCT) --[punct]--> Oh (INTJ)`
2656
2657	`Total sentences: 2`
2658
2659	`RAKE Keyphrase Relationships:`
2660	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
2661
2662	`YAKE Keyphrase Relationships:`
2663	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
2664
2665	`Timing Comparison:`
2666	`RAKE: 0.0000sec`
2667	`YAKE: 0.0000sec`
2668	`KeyBERT: 0.0115sec`
2669	`Dependencies: 0.0000sec`
2670	`Fastest: RAKE`
2671
2672	`================================================================================`
2673	`Message 18:`
2674	`SHAPIRO: Really?`
2675	`--------------------------------------------------------------------------------`
2676	`RAKE Keywords:`
2677	`- shapiro (score: 1.00) → SHAPIRO`
2678	`- really (score: 1.00)`
2679	`Total keywords: 2 extracted in 0.0000 seconds`
2680
2681	`YAKE Keywords:`
2682	`- SHAPIRO (score: 0.03) → SHAPIRO`
2683	`Total keywords: 1 extracted in 0.0000 seconds`
2684
2685	`KeyBERT Keywords:`
2686	`- shapiro really (score: 0.85)`
2687	`- shapiro (score: 0.81)`
2688	`- really (score: 0.25)`
2689	`Total keywords: 3 extracted in 0.0180 seconds`
2690
2691	`Dependency Relations (extracted in 0.0020sec):`
2692
2693	`Sentence 1: SHAPIRO: Really?`
2694	`SHAPIRO (PROPN) --[ROOT]--> SHAPIRO (PROPN)`
2695	`: (PUNCT) --[punct]--> SHAPIRO (PROPN)`
2696	`Really (ADV) --[advmod]--> SHAPIRO (PROPN)`
2697	`? (PUNCT) --[punct]--> SHAPIRO (PROPN)`
2698
2699	`Total sentences: 1`
2700
2701	`RAKE Keyphrase Relationships:`
2702	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
2703
2704	`YAKE Keyphrase Relationships:`
2705	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
2706
2707	`Timing Comparison:`
2708	`RAKE: 0.0000sec`
2709	`YAKE: 0.0000sec`
2710	`KeyBERT: 0.0180sec`
2711	`Dependencies: 0.0020sec`
2712	`Fastest: RAKE`
2713
2714	`================================================================================`
2715	`Message 19:`
2716	`GEOFF BENNETT: Great to be with you, Michel.`
2717	`--------------------------------------------------------------------------------`
2718	`RAKE Keywords:`
2719	`- geoff bennett (score: 4.00) → GEOFF BENNETT`
2720	`- michel (score: 1.00) → Michel`
2721	`- great (score: 1.00)`
2722	`Total keywords: 3 extracted in 0.0000 seconds`
2723
2724	`YAKE Keywords:`
2725	`- GEOFF BENNETT (score: 0.01) → GEOFF BENNETT`
2726	`- Michel (score: 0.03) → Michel`
2727	`- GEOFF (score: 0.09) → GEOFF`
2728	`- BENNETT (score: 0.09) → BENNETT`
2729	`- Great (score: 0.09)`
2730	`Total keywords: 5 extracted in 0.0017 seconds`
2731
2732	`KeyBERT Keywords:`
2733	`- geoff bennett great (score: 0.74)`
2734	`- bennett great michel (score: 0.72)`
2735	`- geoff bennett (score: 0.69)`
2736	`- great michel (score: 0.64)`
2737	`- bennett great (score: 0.62)`
2738	`- michel (score: 0.57)`
2739	`- bennett (score: 0.55)`
2740	`- geoff (score: 0.49)`
2741	`- great (score: 0.30)`
2742	`Total keywords: 9 extracted in 0.0145 seconds`
2743
2744	`Dependency Relations (extracted in 0.0060sec):`
2745
2746	`Sentence 1: GEOFF BENNETT: Great to be with you, Michel.`
2747	`GEOFF (PROPN) --[compound]--> BENNETT (PROPN)`
2748	`BENNETT (PROPN) --[ROOT]--> BENNETT (PROPN)`
2749	`: (PUNCT) --[punct]--> BENNETT (PROPN)`
2750	`Great (ADJ) --[appos]--> BENNETT (PROPN)`
2751	`to (PART) --[aux]--> be (AUX)`
2752	`be (AUX) --[relcl]--> Great (ADJ)`
2753	`with (ADP) --[prep]--> be (AUX)`
2754	`you (PRON) --[pobj]--> with (ADP)`
2755	`, (PUNCT) --[punct]--> you (PRON)`
2756	`Michel (PROPN) --[appos]--> you (PRON)`
2757	`. (PUNCT) --[punct]--> BENNETT (PROPN)`
2758
2759	`Total sentences: 1`
2760
2761	`RAKE Keyphrase Relationships:`
2762	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
2763
2764	`YAKE Keyphrase Relationships:`
2765	`noun phrase: "GEOFF BENNETT" contains [geoff bennett], [geoff], [bennett]`
2766	`Total relationships found: 1`
2767
2768	`Timing Comparison:`
2769	`RAKE: 0.0000sec`
2770	`YAKE: 0.0017sec`
2771	`KeyBERT: 0.0145sec`
2772	`Dependencies: 0.0060sec`
2773	`Fastest: RAKE`
2774
2775	`================================================================================`
2776	`Message 20:`
2777	`MYRE: For someone who spent her formative years as a bit of a hipster and an outsider, Avril Haines is now becoming the ultimate insider.Greg Myre, NPR News, Washington.`
2778	`--------------------------------------------------------------------------------`
2779	`RAKE Keywords:`
2780	`- ultimate insider (score: 4.00)`
2781	`- npr news (score: 4.00) → NPR News`
2782	`- formative years (score: 4.00) → formative year`
2783	`- avril haines (score: 4.00) → Avril Haines`
2784	`- greg myre (score: 3.50) → Greg Myre`
2785	`- myre (score: 1.50)`
2786	`- washington (score: 1.00) → Washington`
2787	`- spent (score: 1.00) → spend`
2788	`- someone (score: 1.00)`
2789	`- outsider (score: 1.00)`
2790	`- hipster (score: 1.00)`
2791	`- bit (score: 1.00)`
2792	`- becoming (score: 1.00) → become`
2793	`Total keywords: 13 extracted in 0.0000 seconds`
2794
2795	`YAKE Keywords:`
2796	`- ultimate insider.Greg Myre (score: 0.00)`
2797	`- Avril Haines (score: 0.01) → Avril Haines`
2798	`- insider.Greg Myre (score: 0.02)`
2799	`- spent her formative (score: 0.03) → spend her formative`
2800	`- formative years (score: 0.03) → formative year`
2801	`- ultimate insider.Greg (score: 0.03)`
2802	`- Washington (score: 0.03) → Washington`
2803	`- MYRE (score: 0.05)`
2804	`- Avril (score: 0.06) → Avril`
2805	`- NPR (score: 0.06) → NPR`
2806	`- Haines (score: 0.09) → Haines`
2807	`- outsider (score: 0.11)`
2808	`- spent (score: 0.18) → spend`
2809	`- formative (score: 0.18)`
2810	`- years (score: 0.18) → year`
2811	`- bit (score: 0.18)`
2812	`- hipster (score: 0.18)`
2813	`- ultimate (score: 0.18)`
2814	`- insider.Greg (score: 0.18)`
2815	`Total keywords: 19 extracted in 0.0138 seconds`
2816
2817	`KeyBERT Keywords:`
2818	`- avril haines (score: 0.73)`
2819	`- avril haines ultimate (score: 0.69)`
2820	`- avril (score: 0.67)`
2821	`- outsider avril haines (score: 0.67)`
2822	`- hipster outsider avril (score: 0.62)`
2823	`- haines ultimate insider (score: 0.58)`
2824	`- outsider avril (score: 0.58)`
2825	`- insider greg myre (score: 0.47)`
2826	`- greg myre npr (score: 0.44)`
2827	`- haines (score: 0.44)`
2828	`- myre npr news (score: 0.42)`
2829	`- haines ultimate (score: 0.41)`
2830	`- ultimate insider (score: 0.40)`
2831	`- myre npr (score: 0.39)`
2832	`- bit hipster outsider (score: 0.39)`
2833	`- greg myre (score: 0.38)`
2834	`- hipster outsider (score: 0.37)`
2835	`- insider (score: 0.37)`
2836	`- years bit hipster (score: 0.35)`
2837	`- ultimate insider greg (score: 0.34)`
2838	`Total keywords: 20 extracted in 0.0340 seconds`
2839
2840	`Dependency Relations (extracted in 0.0093sec):`
2841
2842	`Sentence 1: MYRE:`
2843	`MYRE (NOUN) --[ROOT]--> MYRE (NOUN)`
2844	`: (PUNCT) --[punct]--> MYRE (NOUN)`
2845
2846	`Sentence 2: For someone who spent her formative years as a bit of a hipster and an outsider, Avril Haines is now becoming the ultimate insider.`
2847	`For (ADP) --[prep]--> becoming (VERB)`
2848	`someone (PRON) --[pobj]--> For (ADP)`
2849	`who (PRON) --[nsubj]--> spent (VERB)`
2850	`spent (VERB) --[relcl]--> someone (PRON)`
2851	`her (PRON) --[poss]--> years (NOUN)`
2852	`formative (ADJ) --[amod]--> years (NOUN)`
2853	`years (NOUN) --[dobj]--> spent (VERB)`
2854	`as (ADP) --[prep]--> spent (VERB)`
2855	`a (DET) --[det]--> bit (NOUN)`
2856	`bit (NOUN) --[pobj]--> as (ADP)`
2857	`of (ADP) --[prep]--> bit (NOUN)`
2858	`a (DET) --[det]--> hipster (NOUN)`
2859	`hipster (NOUN) --[pobj]--> of (ADP)`
2860	`and (CCONJ) --[cc]--> bit (NOUN)`
2861	`an (DET) --[det]--> outsider (NOUN)`
2862	`outsider (NOUN) --[conj]--> bit (NOUN)`
2863	`, (PUNCT) --[punct]--> becoming (VERB)`
2864	`Avril (PROPN) --[compound]--> Haines (PROPN)`
2865	`Haines (PROPN) --[nsubj]--> becoming (VERB)`
2866	`is (AUX) --[aux]--> becoming (VERB)`
2867	`now (ADV) --[advmod]--> becoming (VERB)`
2868	`becoming (VERB) --[ROOT]--> becoming (VERB)`
2869	`the (DET) --[det]--> insider (NOUN)`
2870	`ultimate (ADJ) --[amod]--> insider (NOUN)`
2871	`insider (NOUN) --[attr]--> becoming (VERB)`
2872	`. (PUNCT) --[punct]--> becoming (VERB)`
2873
2874	`Sentence 3: Greg Myre, NPR News, Washington.`
2875	`Greg (PROPN) --[compound]--> Myre (PROPN)`
2876	`Myre (PROPN) --[ROOT]--> Myre (PROPN)`
2877	`, (PUNCT) --[punct]--> Myre (PROPN)`
2878	`NPR (PROPN) --[compound]--> News (PROPN)`
2879	`News (PROPN) --[appos]--> Myre (PROPN)`
2880	`, (PUNCT) --[punct]--> News (PROPN)`
2881	`Washington (PROPN) --[npadvmod]--> News (PROPN)`
2882	`. (PUNCT) --[punct]--> Myre (PROPN)`
2883
2884	`Total sentences: 3`
2885
2886	`RAKE Keyphrase Relationships:`
2887	`noun phrase: "Greg Myre" contains [greg myre], [myre]`
2888	`Total relationships found: 1`
2889
2890	`YAKE Keyphrase Relationships:`
2891	`noun phrase: "her formative years" contains [formative years], [formative], [years]`
2892	`noun phrase: "Avril Haines" contains [avril haines], [avril], [haines]`
2893	`verb phrase: "spent years as" contains [spent], [years]`
2894	`Total relationships found: 3`
2895
2896	`Timing Comparison:`
2897	`RAKE: 0.0000sec`
2898	`YAKE: 0.0138sec`
2899	`KeyBERT: 0.0340sec`
2900	`Dependencies: 0.0093sec`
2901	`Fastest: RAKE`
2902
2903	`================================================================================`
2904	`Message 21:`
2905	DANIEL ESTRIN: Well, it was a milestone. It held a lot of symbolic meaning. And the Israelis really milked the moment. There was an El Al plane, Israel's flagship airlines, that flew. They decorated the plane with the word peace in Hebrew, Arabic and English. And the headrests on our seats said, making history. I spoke to Israeli flight attendants who said they'd been working for decades for the airlines and that this was the most exciting flight they've ever taken, traveling openly to a newly welcoming Arab country. And then the pilot made a special announcement.
2906	`--------------------------------------------------------------------------------`
2907	`RAKE Keywords:`
2908	`- israelis really milked (score: 9.00) → Israelis really milk`
2909	`- israeli flight attendants (score: 8.50) → israeli flight attendant`
2910	`- el al plane (score: 8.00) → El Al plane`
2911	`- exciting flight (score: 4.50)`
2912	`- word peace (score: 4.00)`
2913	`- traveling openly (score: 4.00) → travel openly`
2914	`- symbolic meaning (score: 4.00)`
2915	`- special announcement (score: 4.00)`
2916	`- pilot made (score: 4.00) → pilot make`
2917	`- making history (score: 4.00) → make history`
2918	`- ever taken (score: 4.00) → ever take`
2919	`- daniel estrin (score: 4.00) → DANIEL ESTRIN`
2920	`- seats said (score: 3.50) → seat say`
2921	`- flagship airlines (score: 3.50) → flagship airline`
2922	`- plane (score: 2.00)`
2923	`- said (score: 1.50) → say`
2924	`- airlines (score: 1.50) → airline`
2925	`- working (score: 1.00) → work`
2926	`- well (score: 1.00)`
2927	`- spoke (score: 1.00) → speak`
2928	`- moment (score: 1.00)`
2929	`- milestone (score: 1.00)`
2930	`- lot (score: 1.00)`
2931	`- israel (score: 1.00) → Israel`
2932	`- held (score: 1.00) → hold`
2933	`- hebrew (score: 1.00) → Hebrew`
2934	`- headrests (score: 1.00) → headrest`
2935	`- flew (score: 1.00) → fly`
2936	`- english (score: 1.00) → English`
2937	`- decorated (score: 1.00) → decorate`
2938	`- decades (score: 1.00) → decade`
2939	`- arabic (score: 1.00) → Arabic`
2940	`Total keywords: 32 extracted in 0.0000 seconds`
2941
2942	`YAKE Keywords:`
2943	`- DANIEL ESTRIN (score: 0.00) → DANIEL ESTRIN`
2944	`- DANIEL (score: 0.06) → DANIEL`
2945	`- ESTRIN (score: 0.06) → ESTRIN`
2946	`- Arabic and English (score: 0.10) → Arabic and English`
2947	`- Israel flagship airlines (score: 0.11)`
2948	`- milestone (score: 0.12)`
2949	`- symbolic meaning (score: 0.14)`
2950	`- Israel flagship (score: 0.17)`
2951	`- held a lot (score: 0.17) → hold a lot`
2952	`- lot of symbolic (score: 0.17)`
2953	`- Israeli flight attendants (score: 0.18) → israeli flight attendant`
2954	`- Israeli flight (score: 0.19)`
2955	`- peace in Hebrew (score: 0.20) → peace in Hebrew`
2956	`- plane (score: 0.23)`
2957	`- welcoming Arab country (score: 0.24)`
2958	`- airlines (score: 0.24) → airline`
2959	`- milked the moment (score: 0.24) → milk the moment`
2960	`- Arab country (score: 0.25)`
2961	`- flight (score: 0.27)`
2962	`- Israel (score: 0.27) → Israel`
2963	`Total keywords: 20 extracted in 0.0290 seconds`
2964
2965	`KeyBERT Keywords:`
2966	`- al plane israel (score: 0.62)`
2967	`- spoke israeli flight (score: 0.57)`
2968	`- plane israel (score: 0.57)`
2969	`- israeli flight (score: 0.56)`
2970	`- symbolic meaning israelis (score: 0.54)`
2971	`- israel flagship airlines (score: 0.54)`
2972	`- history spoke israeli (score: 0.53)`
2973	`- meaning israelis really (score: 0.52)`
2974	`- plane israel flagship (score: 0.51)`
2975	`- meaning israelis (score: 0.49)`
2976	`- peace hebrew arabic (score: 0.48)`
2977	`- airlines exciting flight (score: 0.47)`
2978	`- arab country pilot (score: 0.47)`
2979	`- spoke israeli (score: 0.46)`
2980	`- israel flagship (score: 0.46)`
2981	`- plane word peace (score: 0.46)`
2982	`- airlines flew (score: 0.46)`
2983	`- el al plane (score: 0.45)`
2984	`- decades airlines exciting (score: 0.44)`
2985	`- welcoming arab country (score: 0.44)`
2986	`Total keywords: 20 extracted in 0.0851 seconds`
2987
2988	`Dependency Relations (extracted in 0.0183sec):`
2989
2990	`Sentence 1: DANIEL ESTRIN:`
2991	`DANIEL (PROPN) --[compound]--> ESTRIN (PROPN)`
2992	`ESTRIN (PROPN) --[ROOT]--> ESTRIN (PROPN)`
2993	`: (PUNCT) --[punct]--> ESTRIN (PROPN)`
2994
2995	`Sentence 2: Well, it was a milestone.`
2996	`Well (INTJ) --[intj]--> was (AUX)`
2997	`, (PUNCT) --[punct]--> was (AUX)`
2998	`it (PRON) --[nsubj]--> was (AUX)`
2999	`was (AUX) --[ROOT]--> was (AUX)`
3000	`a (DET) --[det]--> milestone (NOUN)`
3001	`milestone (NOUN) --[attr]--> was (AUX)`
3002	`. (PUNCT) --[punct]--> was (AUX)`
3003
3004	`Sentence 3: It held a lot of symbolic meaning.`
3005	`It (PRON) --[nsubj]--> held (VERB)`
3006	`held (VERB) --[ROOT]--> held (VERB)`
3007	`a (DET) --[det]--> lot (NOUN)`
3008	`lot (NOUN) --[dobj]--> held (VERB)`
3009	`of (ADP) --[prep]--> lot (NOUN)`
3010	`symbolic (ADJ) --[amod]--> meaning (NOUN)`
3011	`meaning (NOUN) --[pobj]--> of (ADP)`
3012	`. (PUNCT) --[punct]--> held (VERB)`
3013
3014	`Sentence 4: And the Israelis really milked the moment.`
3015	`And (CCONJ) --[cc]--> milked (VERB)`
3016	`the (DET) --[det]--> Israelis (PROPN)`
3017	`Israelis (PROPN) --[nsubj]--> milked (VERB)`
3018	`really (ADV) --[advmod]--> milked (VERB)`
3019	`milked (VERB) --[ROOT]--> milked (VERB)`
3020	`the (DET) --[det]--> moment (NOUN)`
3021	`moment (NOUN) --[dobj]--> milked (VERB)`
3022	`. (PUNCT) --[punct]--> milked (VERB)`
3023
3024	`Sentence 5: There was an El Al plane, Israel's flagship airlines, that flew.`
3025	`There (PRON) --[expl]--> was (VERB)`
3026	`was (VERB) --[ROOT]--> was (VERB)`
3027	`an (DET) --[det]--> plane (NOUN)`
3028	`El (PROPN) --[compound]--> Al (PROPN)`
3029	`Al (PROPN) --[compound]--> plane (NOUN)`
3030	`plane (NOUN) --[attr]--> was (VERB)`
3031	`, (PUNCT) --[punct]--> plane (NOUN)`
3032	`Israel (PROPN) --[poss]--> airlines (NOUN)`
3033	`'s (PART) --[case]--> Israel (PROPN)`
3034	`flagship (NOUN) --[compound]--> airlines (NOUN)`
3035	`airlines (NOUN) --[appos]--> plane (NOUN)`
3036	`, (PUNCT) --[punct]--> plane (NOUN)`
3037	`that (PRON) --[nsubj]--> flew (VERB)`
3038	`flew (VERB) --[relcl]--> plane (NOUN)`
3039	`. (PUNCT) --[punct]--> was (VERB)`
3040
3041	`Sentence 6: They decorated the plane with the word peace in Hebrew, Arabic and English.`
3042	`They (PRON) --[nsubj]--> decorated (VERB)`
3043	`decorated (VERB) --[ROOT]--> decorated (VERB)`
3044	`the (DET) --[det]--> plane (NOUN)`
3045	`plane (NOUN) --[dobj]--> decorated (VERB)`
3046	`with (ADP) --[prep]--> decorated (VERB)`
3047	`the (DET) --[det]--> peace (NOUN)`
3048	`word (NOUN) --[compound]--> peace (NOUN)`
3049	`peace (NOUN) --[pobj]--> with (ADP)`
3050	`in (ADP) --[prep]--> peace (NOUN)`
3051	`Hebrew (PROPN) --[pobj]--> in (ADP)`
3052	`, (PUNCT) --[punct]--> Hebrew (PROPN)`
3053	`Arabic (PROPN) --[conj]--> Hebrew (PROPN)`
3054	`and (CCONJ) --[cc]--> Arabic (PROPN)`
3055	`English (PROPN) --[conj]--> Arabic (PROPN)`
3056	`. (PUNCT) --[punct]--> decorated (VERB)`
3057
3058	`Sentence 7: And the headrests on our seats said, making history.`
3059	`And (CCONJ) --[cc]--> said (VERB)`
3060	`the (DET) --[det]--> headrests (NOUN)`
3061	`headrests (NOUN) --[nsubj]--> said (VERB)`
3062	`on (ADP) --[prep]--> headrests (NOUN)`
3063	`our (PRON) --[poss]--> seats (NOUN)`
3064	`seats (NOUN) --[pobj]--> on (ADP)`
3065	`said (VERB) --[ROOT]--> said (VERB)`
3066	`, (PUNCT) --[punct]--> said (VERB)`
3067	`making (VERB) --[advcl]--> said (VERB)`
3068	`history (NOUN) --[dobj]--> making (VERB)`
3069	`. (PUNCT) --[punct]--> said (VERB)`
3070
3071	`Sentence 8: I spoke to Israeli flight attendants who said they'd been working for decades for the airlines and that this was the most exciting flight they've ever taken, traveling openly to a newly welcoming Arab country.`
3072	`I (PRON) --[nsubj]--> spoke (VERB)`
3073	`spoke (VERB) --[ROOT]--> spoke (VERB)`
3074	`to (ADP) --[prep]--> spoke (VERB)`
3075	`Israeli (ADJ) --[amod]--> attendants (NOUN)`
3076	`flight (NOUN) --[compound]--> attendants (NOUN)`
3077	`attendants (NOUN) --[pobj]--> to (ADP)`
3078	`who (PRON) --[nsubj]--> said (VERB)`
3079	`said (VERB) --[relcl]--> attendants (NOUN)`
3080	`they (PRON) --[nsubj]--> working (VERB)`
3081	`'d (AUX) --[aux]--> working (VERB)`
3082	`been (AUX) --[aux]--> working (VERB)`
3083	`working (VERB) --[ccomp]--> said (VERB)`
3084	`for (ADP) --[prep]--> working (VERB)`
3085	`decades (NOUN) --[pobj]--> for (ADP)`
3086	`for (ADP) --[prep]--> decades (NOUN)`
3087	`the (DET) --[det]--> airlines (NOUN)`
3088	`airlines (NOUN) --[pobj]--> for (ADP)`
3089	`and (CCONJ) --[cc]--> working (VERB)`
3090	`that (SCONJ) --[mark]--> was (AUX)`
3091	`this (PRON) --[nsubj]--> was (AUX)`
3092	`was (AUX) --[conj]--> working (VERB)`
3093	`the (DET) --[det]--> flight (NOUN)`
3094	`most (ADV) --[advmod]--> exciting (ADJ)`
3095	`exciting (ADJ) --[amod]--> flight (NOUN)`
3096	`flight (NOUN) --[attr]--> was (AUX)`
3097	`they (PRON) --[nsubj]--> taken (VERB)`
3098	`'ve (AUX) --[aux]--> taken (VERB)`
3099	`ever (ADV) --[advmod]--> taken (VERB)`
3100	`taken (VERB) --[relcl]--> flight (NOUN)`
3101	`, (PUNCT) --[punct]--> flight (NOUN)`
3102	`traveling (VERB) --[advcl]--> was (AUX)`
3103	`openly (ADV) --[advmod]--> traveling (VERB)`
3104	`to (ADP) --[prep]--> traveling (VERB)`
3105	`a (DET) --[det]--> country (NOUN)`
3106	`newly (ADV) --[advmod]--> welcoming (ADJ)`
3107	`welcoming (ADJ) --[amod]--> country (NOUN)`
3108	`Arab (ADJ) --[amod]--> country (NOUN)`
3109	`country (NOUN) --[pobj]--> to (ADP)`
3110	`. (PUNCT) --[punct]--> spoke (VERB)`
3111
3112	`Sentence 9: And then the pilot made a special announcement.`
3113	`And (CCONJ) --[cc]--> made (VERB)`
3114	`then (ADV) --[advmod]--> made (VERB)`
3115	`the (DET) --[det]--> pilot (NOUN)`
3116	`pilot (NOUN) --[nsubj]--> made (VERB)`
3117	`made (VERB) --[ROOT]--> made (VERB)`
3118	`a (DET) --[det]--> announcement (NOUN)`
3119	`special (ADJ) --[amod]--> announcement (NOUN)`
3120	`announcement (NOUN) --[dobj]--> made (VERB)`
3121	`. (PUNCT) --[punct]--> made (VERB)`
3122
3123	`Total sentences: 9`
3124
3125	`RAKE Keyphrase Relationships:`
3126	`noun phrase: "an El Al plane" contains [el al plane], [plane]`
3127	`noun phrase: "Israel's flagship airlines" contains [flagship airlines], [airlines], [israel]`
3128	`noun phrase: "Israeli flight attendants" contains [israeli flight attendants], [israel]`
3129	`verb phrase: "held lot" contains [lot], [held]`
3130	`verb phrase: "decorated plane with" contains [plane], [decorated]`
3131	`Total relationships found: 5`
3132
3133	`YAKE Keyphrase Relationships:`
3134	`noun phrase: "DANIEL ESTRIN" contains [daniel estrin], [daniel], [estrin]`
3135	`noun phrase: "Israel's flagship airlines" contains [airlines], [israel]`
3136	`noun phrase: "Israeli flight attendants" contains [israeli flight attendants], [israeli flight], [flight], [israel]`
3137	`noun phrase: "a newly welcoming Arab country" contains [welcoming arab country], [arab country]`
3138	`Total relationships found: 4`
3139
3140	`Timing Comparison:`
3141	`RAKE: 0.0000sec`
3142	`YAKE: 0.0290sec`
3143	`KeyBERT: 0.0851sec`
3144	`Dependencies: 0.0183sec`
3145	`Fastest: RAKE`
3146
3147	`================================================================================`
3148	`Message 22:`
3149	`GOODMAN: Yes.`
3150	`--------------------------------------------------------------------------------`
3151	`RAKE Keywords:`
3152	`- yes (score: 1.00)`
3153	`- goodman (score: 1.00) → GOODMAN`
3154	`Total keywords: 2 extracted in 0.0000 seconds`
3155
3156	`YAKE Keywords:`
3157	`- GOODMAN (score: 0.03) → GOODMAN`
3158	`Total keywords: 1 extracted in 0.0000 seconds`
3159
3160	`KeyBERT Keywords:`
3161	`- goodman yes (score: 0.91)`
3162	`- goodman (score: 0.84)`
3163	`- yes (score: 0.25)`
3164	`Total keywords: 3 extracted in 0.0138 seconds`
3165
3166	`Dependency Relations (extracted in 0.0045sec):`
3167
3168	`Sentence 1: GOODMAN: Yes.`
3169	`GOODMAN (PROPN) --[ROOT]--> GOODMAN (PROPN)`
3170	`: (PUNCT) --[punct]--> GOODMAN (PROPN)`
3171	`Yes (INTJ) --[intj]--> GOODMAN (PROPN)`
3172	`. (PUNCT) --[punct]--> Yes (INTJ)`
3173
3174	`Total sentences: 1`
3175
3176	`RAKE Keyphrase Relationships:`
3177	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
3178
3179	`YAKE Keyphrase Relationships:`
3180	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
3181
3182	`Timing Comparison:`
3183	`RAKE: 0.0000sec`
3184	`YAKE: 0.0000sec`
3185	`KeyBERT: 0.0138sec`
3186	`Dependencies: 0.0045sec`
3187	`Fastest: RAKE`
3188
3189	`================================================================================`
3190	`Message 23:`
3191	`CORNISH: So the company paid workers in bad checks. Am I getting that right?`
3192	`--------------------------------------------------------------------------------`
3193	`RAKE Keywords:`
3194	`- company paid workers (score: 9.00) → company pay worker`
3195	`- bad checks (score: 4.00) → bad check`
3196	`- right (score: 1.00)`
3197	`- getting (score: 1.00) → get`
3198	`- cornish (score: 1.00) → CORNISH`
3199	`Total keywords: 5 extracted in 0.0000 seconds`
3200
3201	`YAKE Keywords:`
3202	`- CORNISH (score: 0.04) → CORNISH`
3203	`- company paid workers (score: 0.07) → company pay worker`
3204	`- bad checks (score: 0.08) → bad check`
3205	`- company paid (score: 0.15) → company pay`
3206	`- paid workers (score: 0.15) → pay worker`
3207	`- workers in bad (score: 0.15) → worker in bad`
3208	`- checks (score: 0.20) → check`
3209	`- company (score: 0.36)`
3210	`- paid (score: 0.36) → pay`
3211	`- workers (score: 0.36) → worker`
3212	`- bad (score: 0.36)`
3213	`Total keywords: 11 extracted in 0.0035 seconds`
3214
3215	`KeyBERT Keywords:`
3216	`- workers bad checks (score: 0.73)`
3217	`- cornish company paid (score: 0.64)`
3218	`- paid workers bad (score: 0.62)`
3219	`- bad checks getting (score: 0.61)`
3220	`- company paid workers (score: 0.61)`
3221	`- bad checks (score: 0.56)`
3222	`- paid workers (score: 0.54)`
3223	`- checks getting right (score: 0.50)`
3224	`- checks getting (score: 0.49)`
3225	`- cornish company (score: 0.48)`
3226	`- company paid (score: 0.46)`
3227	`- checks (score: 0.45)`
3228	`- workers bad (score: 0.45)`
3229	`- cornish (score: 0.39)`
3230	`- workers (score: 0.38)`
3231	`- paid (score: 0.26)`
3232	`- company (score: 0.23)`
3233	`- getting right (score: 0.16)`
3234	`- bad (score: 0.09)`
3235	`- right (score: 0.09)`
3236	`Total keywords: 20 extracted in 0.0215 seconds`
3237
3238	`Dependency Relations (extracted in 0.0020sec):`
3239
3240	`Sentence 1: CORNISH: So the company paid workers in bad checks.`
3241	`CORNISH (VERB) --[dep]--> paid (VERB)`
3242	`: (PUNCT) --[punct]--> paid (VERB)`
3243	`So (ADV) --[advmod]--> paid (VERB)`
3244	`the (DET) --[det]--> company (NOUN)`
3245	`company (NOUN) --[nsubj]--> paid (VERB)`
3246	`paid (VERB) --[ROOT]--> paid (VERB)`
3247	`workers (NOUN) --[dobj]--> paid (VERB)`
3248	`in (ADP) --[prep]--> paid (VERB)`
3249	`bad (ADJ) --[amod]--> checks (NOUN)`
3250	`checks (NOUN) --[pobj]--> in (ADP)`
3251	`. (PUNCT) --[punct]--> paid (VERB)`
3252
3253	`Sentence 2: Am I getting that right?`
3254	`Am (AUX) --[aux]--> getting (VERB)`
3255	`I (PRON) --[nsubj]--> getting (VERB)`
3256	`getting (VERB) --[ROOT]--> getting (VERB)`
3257	`that (PRON) --[dobj]--> getting (VERB)`
3258	`right (ADJ) --[advmod]--> getting (VERB)`
3259	`? (PUNCT) --[punct]--> getting (VERB)`
3260
3261	`Total sentences: 2`
3262
3263	`RAKE Keyphrase Relationships:`
3264	`verb phrase: "getting Am that right" contains [right], [getting]`
3265	`Total relationships found: 1`
3266
3267	`YAKE Keyphrase Relationships:`
3268	`noun phrase: "bad checks" contains [bad checks], [checks], [bad]`
3269	`verb phrase: "paid So workers in" contains [paid], [workers]`
3270	`Total relationships found: 2`
3271
3272	`Timing Comparison:`
3273	`RAKE: 0.0000sec`
3274	`YAKE: 0.0035sec`
3275	`KeyBERT: 0.0215sec`
3276	`Dependencies: 0.0020sec`
3277	`Fastest: RAKE`
3278
3279	`================================================================================`
3280	`Message 24:`
3281	`SHAPIRO: In all, the president spent a few hours at Bagram Air Base. He served turkey to troops, ate some mashed potatoes and met with Afghan President Ashraf Ghani. And he actually made some news, which is why we've called NPR national political correspondent Mara Liasson away from her Thanksgiving. Thanks, Mara. Good to have you here.`
3282	`--------------------------------------------------------------------------------`
3283	`RAKE Keywords:`
3284	`- bagram air base (score: 9.00) → Bagram Air Base`
3285	`- served turkey (score: 4.00) → serve turkey`
3286	`- president spent (score: 4.00) → president spend`
3287	`- mashed potatoes (score: 4.00) → mashed potato`
3288	`- actually made (score: 4.00) → actually make`
3289	`- troops (score: 1.00) → troop`
3290	`- thanksgiving (score: 1.00) → Thanksgiving`
3291	`- thanks (score: 1.00) → thank`
3292	`- shapiro (score: 1.00) → SHAPIRO`
3293	`- news (score: 1.00)`
3294	`- met (score: 1.00) → meet`
3295	`- mara (score: 1.00) → Mara`
3296	`- hours (score: 1.00) → hour`
3297	`- good (score: 1.00)`
3298	`- ate (score: 1.00) → eat`
3299	`Total keywords: 15 extracted in 0.0000 seconds`
3300
3301	`YAKE Keywords:`
3302	`- Bagram Air Base (score: 0.00) → Bagram Air Base`
3303	`- Air Base (score: 0.01) → Air Base`
3304	`- Bagram Air (score: 0.01) → Bagram Air`
3305	`- President Ashraf Ghani (score: 0.03) → President Ashraf Ghani`
3306	`- hours at Bagram (score: 0.03) → hour at Bagram`
3307	`- Afghan President Ashraf (score: 0.04) → afghan President Ashraf`
3308	`- SHAPIRO (score: 0.05) → SHAPIRO`
3309	`- Ashraf Ghani (score: 0.08) → Ashraf Ghani`
3310	`- Base (score: 0.08) → Base`
3311	`- president spent (score: 0.11) → president spend`
3312	`- Bagram (score: 0.11) → Bagram`
3313	`- Air (score: 0.11) → Air`
3314	`- Afghan President (score: 0.12) → afghan President`
3315	`- President Ashraf (score: 0.12) → President Ashraf`
3316	`- president (score: 0.18)`
3317	`- met with Afghan (score: 0.22) → meet with afghan`
3318	`- Mara Liasson (score: 0.23) → Mara Liasson`
3319	`- Ghani (score: 0.23) → Ghani`
3320	`- Mara (score: 0.23) → Mara`
3321	`- correspondent Mara Liasson (score: 0.25) → correspondent Mara Liasson`
3322	`Total keywords: 20 extracted in 0.0210 seconds`
3323
3324	`KeyBERT Keywords:`
3325	`- met afghan president (score: 0.59)`
3326	`- potatoes met afghan (score: 0.54)`
3327	`- shapiro president spent (score: 0.53)`
3328	`- met afghan (score: 0.50)`
3329	`- shapiro president (score: 0.49)`
3330	`- political correspondent mara (score: 0.48)`
3331	`- afghan president (score: 0.48)`
3332	`- president ashraf ghani (score: 0.47)`
3333	`- afghan president ashraf (score: 0.47)`
3334	`- national political correspondent (score: 0.45)`
3335	`- president spent hours (score: 0.45)`
3336	`- served turkey troops (score: 0.44)`
3337	`- political correspondent (score: 0.44)`
3338	`- correspondent mara (score: 0.42)`
3339	`- ghani actually news (score: 0.42)`
3340	`- turkey troops ate (score: 0.41)`
3341	`- served turkey (score: 0.41)`
3342	`- president ashraf (score: 0.40)`
3343	`- mashed potatoes met (score: 0.40)`
3344	`- afghan (score: 0.39)`
3345	`Total keywords: 20 extracted in 0.0544 seconds`
3346
3347	`Dependency Relations (extracted in 0.0095sec):`
3348
3349	`Sentence 1: SHAPIRO:`
3350	`SHAPIRO (PROPN) --[ROOT]--> SHAPIRO (PROPN)`
3351	`: (PUNCT) --[punct]--> SHAPIRO (PROPN)`
3352
3353	`Sentence 2: In all, the president spent a few hours at Bagram Air Base.`
3354	`In (ADP) --[prep]--> spent (VERB)`
3355	`all (PRON) --[pobj]--> In (ADP)`
3356	`, (PUNCT) --[punct]--> spent (VERB)`
3357	`the (DET) --[det]--> president (NOUN)`
3358	`president (NOUN) --[nsubj]--> spent (VERB)`
3359	`spent (VERB) --[ROOT]--> spent (VERB)`
3360	`a (DET) --[det]--> hours (NOUN)`
3361	`few (ADJ) --[amod]--> hours (NOUN)`
3362	`hours (NOUN) --[dobj]--> spent (VERB)`
3363	`at (ADP) --[prep]--> spent (VERB)`
3364	`Bagram (PROPN) --[compound]--> Base (PROPN)`
3365	`Air (PROPN) --[compound]--> Base (PROPN)`
3366	`Base (PROPN) --[pobj]--> at (ADP)`
3367	`. (PUNCT) --[punct]--> spent (VERB)`
3368
3369	`Sentence 3: He served turkey to troops, ate some mashed potatoes and met with Afghan President Ashraf Ghani.`
3370	`He (PRON) --[nsubj]--> served (VERB)`
3371	`served (VERB) --[ROOT]--> served (VERB)`
3372	`turkey (NOUN) --[dobj]--> served (VERB)`
3373	`to (ADP) --[prep]--> served (VERB)`
3374	`troops (NOUN) --[pobj]--> to (ADP)`
3375	`, (PUNCT) --[punct]--> served (VERB)`
3376	`ate (VERB) --[conj]--> served (VERB)`
3377	`some (DET) --[det]--> potatoes (NOUN)`
3378	`mashed (ADJ) --[amod]--> potatoes (NOUN)`
3379	`potatoes (NOUN) --[dobj]--> ate (VERB)`
3380	`and (CCONJ) --[cc]--> ate (VERB)`
3381	`met (VERB) --[conj]--> ate (VERB)`
3382	`with (ADP) --[prep]--> met (VERB)`
3383	`Afghan (ADJ) --[amod]--> President (PROPN)`
3384	`President (PROPN) --[compound]--> Ghani (PROPN)`
3385	`Ashraf (PROPN) --[compound]--> Ghani (PROPN)`
3386	`Ghani (PROPN) --[pobj]--> with (ADP)`
3387	`. (PUNCT) --[punct]--> served (VERB)`
3388
3389	`Sentence 4: And he actually made some news, which is why we've called NPR national political correspondent Mara Liasson away from her Thanksgiving.`
3390	`And (CCONJ) --[cc]--> made (VERB)`
3391	`he (PRON) --[nsubj]--> made (VERB)`
3392	`actually (ADV) --[advmod]--> made (VERB)`
3393	`made (VERB) --[ROOT]--> made (VERB)`
3394	`some (DET) --[det]--> news (NOUN)`
3395	`news (NOUN) --[dobj]--> made (VERB)`
3396	`, (PUNCT) --[punct]--> news (NOUN)`
3397	`which (PRON) --[nsubj]--> is (AUX)`
3398	`is (AUX) --[relcl]--> news (NOUN)`
3399	`why (SCONJ) --[advmod]--> called (VERB)`
3400	`we (PRON) --[nsubj]--> called (VERB)`
3401	`'ve (AUX) --[aux]--> called (VERB)`
3402	`called (VERB) --[ccomp]--> is (AUX)`
3403	`NPR (PROPN) --[nmod]--> correspondent (NOUN)`
3404	`national (ADJ) --[amod]--> correspondent (NOUN)`
3405	`political (ADJ) --[amod]--> correspondent (NOUN)`
3406	`correspondent (NOUN) --[compound]--> Liasson (PROPN)`
3407	`Mara (PROPN) --[compound]--> Liasson (PROPN)`
3408	`Liasson (PROPN) --[dobj]--> called (VERB)`
3409	`away (ADV) --[advmod]--> called (VERB)`
3410	`from (ADP) --[prep]--> away (ADV)`
3411	`her (PRON) --[poss]--> Thanksgiving (PROPN)`
3412	`Thanksgiving (PROPN) --[pobj]--> from (ADP)`
3413	`. (PUNCT) --[punct]--> made (VERB)`
3414
3415	`Sentence 5: Thanks, Mara.`
3416	`Thanks (NOUN) --[ROOT]--> Thanks (NOUN)`
3417	`, (PUNCT) --[punct]--> Thanks (NOUN)`
3418	`Mara (PROPN) --[npadvmod]--> Thanks (NOUN)`
3419	`. (PUNCT) --[punct]--> Thanks (NOUN)`
3420
3421	`Sentence 6: Good to have you here.`
3422	`Good (ADJ) --[ROOT]--> Good (ADJ)`
3423	`to (PART) --[aux]--> have (VERB)`
3424	`have (VERB) --[advcl]--> Good (ADJ)`
3425	`you (PRON) --[dobj]--> have (VERB)`
3426	`here (ADV) --[advmod]--> have (VERB)`
3427	`. (PUNCT) --[punct]--> Good (ADJ)`
3428
3429	`Total sentences: 6`
3430
3431	`RAKE Keyphrase Relationships:`
3432	`noun phrase: "her Thanksgiving" contains [thanksgiving], [thanks]`
3433	`Total relationships found: 1`
3434
3435	`YAKE Keyphrase Relationships:`
3436	`noun phrase: "Bagram Air Base" contains [bagram air base], [air base], [bagram air], [base], [bagram], [air]`
3437	`noun phrase: "Afghan President Ashraf Ghani" contains [president ashraf ghani], [afghan president ashraf], [ashraf ghani], [afghan president], [president ashraf], [president], [ghani]`
3438	`noun phrase: "NPR national political correspondent Mara Liasson" contains [mara liasson], [mara], [correspondent mara liasson]`
3439	`Total relationships found: 3`
3440
3441	`Timing Comparison:`
3442	`RAKE: 0.0000sec`
3443	`YAKE: 0.0210sec`
3444	`KeyBERT: 0.0544sec`
3445	`Dependencies: 0.0095sec`
3446	`Fastest: RAKE`
3447
3448	`================================================================================`
3449	`Message 25:`
3450	`CARRIE KAHN: In the southwest corner of the Gaza Strip, near the border with Egypt, producer Anas Baba says tents are everywhere. One cluster catches his attention - seven families sheltering on a small dirt field behind a gate.`
3451	`--------------------------------------------------------------------------------`
3452	`RAKE Keywords:`
3453	`- seven families sheltering (score: 9.00) → seven family shelter`
3454	`- one cluster catches (score: 9.00) → one cluster catch`
3455	`- southwest corner (score: 4.00) → southw corner`
3456	`- gaza strip (score: 4.00) → Gaza Strip`
3457	`- carrie kahn (score: 4.00) → CARRIE KAHN`
3458	`- near (score: 1.00)`
3459	`- gate (score: 1.00)`
3460	`- everywhere (score: 1.00)`
3461	`- egypt (score: 1.00) → Egypt`
3462	`- border (score: 1.00)`
3463	`- attention (score: 1.00)`
3464	`Total keywords: 11 extracted in 0.0000 seconds`
3465
3466	`YAKE Keywords:`
3467	`- producer Anas Baba (score: 0.00) → producer Anas Baba`
3468	`- CARRIE KAHN (score: 0.00) → CARRIE KAHN`
3469	`- Gaza Strip (score: 0.00) → Gaza Strip`
3470	`- Anas Baba (score: 0.01) → Anas Baba`
3471	`- producer Anas (score: 0.01) → producer Anas`
3472	`- border with Egypt (score: 0.01) → border with Egypt`
3473	`- Baba says tents (score: 0.01) → Baba say tent`
3474	`- southwest corner (score: 0.02) → southw corner`
3475	`- CARRIE (score: 0.06) → CARRIE`
3476	`- KAHN (score: 0.06) → KAHN`
3477	`- Strip (score: 0.06) → Strip`
3478	`- Egypt (score: 0.06) → Egypt`
3479	`- Gaza (score: 0.08) → Gaza`
3480	`- Anas (score: 0.08) → Anas`
3481	`- Baba (score: 0.08) → Baba`
3482	`- small dirt field (score: 0.08)`
3483	`- producer (score: 0.10)`
3484	`- catches his attention (score: 0.12) → catch his attention`
3485	`- southwest (score: 0.15) → southw`
3486	`- corner (score: 0.15)`
3487	`Total keywords: 20 extracted in 0.0135 seconds`
3488
3489	`KeyBERT Keywords:`
3490	`- baba says tents (score: 0.67)`
3491	`- tents cluster (score: 0.67)`
3492	`- says tents cluster (score: 0.62)`
3493	`- tents (score: 0.60)`
3494	`- tents cluster catches (score: 0.60)`
3495	`- corner gaza (score: 0.54)`
3496	`- says tents (score: 0.54)`
3497	`- corner gaza strip (score: 0.52)`
3498	`- southwest corner gaza (score: 0.51)`
3499	`- gaza strip near (score: 0.51)`
3500	`- gaza strip (score: 0.51)`
3501	`- gaza (score: 0.49)`
3502	`- seven families sheltering (score: 0.48)`
3503	`- families sheltering (score: 0.48)`
3504	`- carrie kahn southwest (score: 0.46)`
3505	`- sheltering (score: 0.45)`
3506	`- anas baba (score: 0.44)`
3507	`- families sheltering small (score: 0.44)`
3508	`- anas baba says (score: 0.42)`
3509	`- sheltering small (score: 0.41)`
3510	`Total keywords: 20 extracted in 0.0436 seconds`
3511
3512	`Dependency Relations (extracted in 0.0020sec):`
3513
3514	`Sentence 1: CARRIE KAHN:`
3515	`CARRIE (PROPN) --[compound]--> KAHN (PROPN)`
3516	`KAHN (PROPN) --[ROOT]--> KAHN (PROPN)`
3517	`: (PUNCT) --[punct]--> KAHN (PROPN)`
3518
3519	`Sentence 2: In the southwest corner of the Gaza Strip, near the border with Egypt, producer Anas Baba says tents are everywhere.`
3520	`In (ADP) --[prep]--> says (VERB)`
3521	`the (DET) --[det]--> corner (NOUN)`
3522	`southwest (ADJ) --[amod]--> corner (NOUN)`
3523	`corner (NOUN) --[pobj]--> In (ADP)`
3524	`of (ADP) --[prep]--> corner (NOUN)`
3525	`the (DET) --[det]--> Strip (PROPN)`
3526	`Gaza (PROPN) --[compound]--> Strip (PROPN)`
3527	`Strip (PROPN) --[pobj]--> of (ADP)`
3528	`, (PUNCT) --[punct]--> says (VERB)`
3529	`near (ADP) --[prep]--> says (VERB)`
3530	`the (DET) --[det]--> border (NOUN)`
3531	`border (NOUN) --[pobj]--> near (ADP)`
3532	`with (ADP) --[prep]--> border (NOUN)`
3533	`Egypt (PROPN) --[pobj]--> with (ADP)`
3534	`, (PUNCT) --[punct]--> says (VERB)`
3535	`producer (NOUN) --[compound]--> Baba (PROPN)`
3536	`Anas (PROPN) --[compound]--> Baba (PROPN)`
3537	`Baba (PROPN) --[nsubj]--> says (VERB)`
3538	`says (VERB) --[ROOT]--> says (VERB)`
3539	`tents (NOUN) --[nsubj]--> are (AUX)`
3540	`are (AUX) --[ccomp]--> says (VERB)`
3541	`everywhere (ADV) --[advmod]--> are (AUX)`
3542	`. (PUNCT) --[punct]--> says (VERB)`
3543
3544	`Sentence 3: One cluster catches his attention - seven families sheltering on a small dirt field behind a gate.`
3545	`One (NUM) --[nummod]--> cluster (NOUN)`
3546	`cluster (NOUN) --[nsubj]--> catches (VERB)`
3547	`catches (VERB) --[ROOT]--> catches (VERB)`
3548	`his (PRON) --[poss]--> families (NOUN)`
3549	`attention (NOUN) --[compound]--> seven (NUM)`
3550	`- (PUNCT) --[punct]--> seven (NUM)`
3551	`seven (NUM) --[nummod]--> families (NOUN)`
3552	`families (NOUN) --[dobj]--> catches (VERB)`
3553	`sheltering (VERB) --[acl]--> families (NOUN)`
3554	`on (ADP) --[prep]--> sheltering (VERB)`
3555	`a (DET) --[det]--> field (NOUN)`
3556	`small (ADJ) --[amod]--> field (NOUN)`
3557	`dirt (NOUN) --[compound]--> field (NOUN)`
3558	`field (NOUN) --[pobj]--> on (ADP)`
3559	`behind (ADP) --[prep]--> sheltering (VERB)`
3560	`a (DET) --[det]--> gate (NOUN)`
3561	`gate (NOUN) --[pobj]--> behind (ADP)`
3562	`. (PUNCT) --[punct]--> catches (VERB)`
3563
3564	`Total sentences: 3`
3565
3566	`RAKE Keyphrase Relationships:`
3567	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
3568
3569	`YAKE Keyphrase Relationships:`
3570	`noun phrase: "CARRIE KAHN" contains [carrie kahn], [carrie], [kahn]`
3571	`noun phrase: "the southwest corner" contains [southwest corner], [southwest], [corner]`
3572	`noun phrase: "the Gaza Strip" contains [gaza strip], [strip], [gaza]`
3573	`noun phrase: "producer Anas Baba" contains [producer anas baba], [anas baba], [producer anas], [anas], [baba], [producer]`
3574	`Total relationships found: 4`
3575
3576	`Timing Comparison:`
3577	`RAKE: 0.0000sec`
3578	`YAKE: 0.0135sec`
3579	`KeyBERT: 0.0436sec`
3580	`Dependencies: 0.0020sec`
3581	`Fastest: RAKE`
3582
3583	`================================================================================`
3584	`Message 26:`
3585	`KHALID: But Mary Louise, I think what's more interesting to me - even though, maybe, Mike Bloomberg is this perfect foil for her message on income inequality - is, does she finally direct her fire at Bernie Sanders? They certainly have some overlapping support amongst progressive voters. But, you know, he is the front-runner. And in the last debate, it did not seem like his rival saw him that way.`
3586	`--------------------------------------------------------------------------------`
3587	`RAKE Keywords:`
3588	`- seem like (score: 4.00)`
3589	`- rival saw (score: 4.00) → rival see`
3590	`- perfect foil (score: 4.00)`
3591	`- mike bloomberg (score: 4.00) → Mike Bloomberg`
3592	`- mary louise (score: 4.00) → Mary Louise`
3593	`- last debate (score: 4.00)`
3594	`- income inequality (score: 4.00)`
3595	`- finally direct (score: 4.00)`
3596	`- even though (score: 4.00)`
3597	`- bernie sanders (score: 4.00) → Bernie Sanders`
3598	`- way (score: 1.00)`
3599	`- think (score: 1.00)`
3600	`- runner (score: 1.00)`
3601	`- message (score: 1.00)`
3602	`- maybe (score: 1.00)`
3603	`- know (score: 1.00)`
3604	`- khalid (score: 1.00) → KHALID`
3605	`- interesting (score: 1.00)`
3606	`- front (score: 1.00)`
3607	`- fire (score: 1.00)`
3608	`- certainly (score: 1.00)`
3609	`Total keywords: 21 extracted in 0.0000 seconds`
3610
3611	`YAKE Keywords:`
3612	`- Mary Louise (score: 0.01) → Mary Louise`
3613	`- Mike Bloomberg (score: 0.01) → Mike Bloomberg`
3614	`- Bernie Sanders (score: 0.01) → Bernie Sanders`
3615	`- fire at Bernie (score: 0.01) → fire at Bernie`
3616	`- income inequality (score: 0.02)`
3617	`- perfect foil (score: 0.03)`
3618	`- message on income (score: 0.03)`
3619	`- finally direct (score: 0.03)`
3620	`- direct her fire (score: 0.03)`
3621	`- KHALID (score: 0.04) → KHALID`
3622	`- Louise (score: 0.06) → Louise`
3623	`- Mike (score: 0.06) → Mike`
3624	`- Sanders (score: 0.06) → Sanders`
3625	`- Mary (score: 0.08) → Mary`
3626	`- Bloomberg (score: 0.08) → Bloomberg`
3627	`- Bernie (score: 0.08) → Bernie`
3628	`- inequality (score: 0.12)`
3629	`- progressive voters (score: 0.15) → progressive voter`
3630	`- interesting (score: 0.17)`
3631	`- perfect (score: 0.17)`
3632	`Total keywords: 20 extracted in 0.0218 seconds`
3633
3634	`KeyBERT Keywords:`
3635	`- mike bloomberg (score: 0.54)`
3636	`- maybe mike bloomberg (score: 0.50)`
3637	`- bernie sanders certainly (score: 0.49)`
3638	`- mike bloomberg perfect (score: 0.47)`
3639	`- direct bernie sanders (score: 0.46)`
3640	`- bernie sanders (score: 0.45)`
3641	`- bernie (score: 0.45)`
3642	`- sanders certainly (score: 0.45)`
3643	`- support progressive voters (score: 0.45)`
3644	`- sanders (score: 0.44)`
3645	`- direct bernie (score: 0.44)`
3646	`- sanders certainly overlapping (score: 0.44)`
3647	`- progressive voters (score: 0.44)`
3648	`- bloomberg (score: 0.42)`
3649	`- finally direct bernie (score: 0.41)`
3650	`- progressive voters know (score: 0.38)`
3651	`- support progressive (score: 0.37)`
3652	`- bloomberg perfect (score: 0.36)`
3653	`- bloomberg perfect foil (score: 0.35)`
3654	`- progressive (score: 0.34)`
3655	`Total keywords: 20 extracted in 0.0393 seconds`
3656
3657	`Dependency Relations (extracted in 0.0000sec):`
3658
3659	`Sentence 1: KHALID:`
3660	`KHALID (PROPN) --[ROOT]--> KHALID (PROPN)`
3661	`: (PUNCT) --[punct]--> KHALID (PROPN)`
3662
3663	`Sentence 2: But Mary Louise, I think what's more interesting to me - even though, maybe, Mike Bloomberg is this perfect foil for her message on income inequality - is, does she finally direct her fire at Bernie Sanders?`
3664	`But (CCONJ) --[cc]--> think (VERB)`
3665	`Mary (PROPN) --[compound]--> Louise (PROPN)`
3666	`Louise (PROPN) --[npadvmod]--> think (VERB)`
3667	`, (PUNCT) --[punct]--> think (VERB)`
3668	`I (PRON) --[nsubj]--> think (VERB)`
3669	`think (VERB) --[ROOT]--> think (VERB)`
3670	`what (PRON) --[nsubj]--> 's (AUX)`
3671	`'s (AUX) --[ccomp]--> think (VERB)`
3672	`more (ADV) --[advmod]--> interesting (ADJ)`
3673	`interesting (ADJ) --[acomp]--> 's (AUX)`
3674	`to (ADP) --[prep]--> interesting (ADJ)`
3675	`me (PRON) --[pobj]--> to (ADP)`
3676	`- (PUNCT) --[punct]--> 's (AUX)`
3677	`even (ADV) --[advmod]--> though (ADV)`
3678	`though (ADV) --[advmod]--> 's (AUX)`
3679	`, (PUNCT) --[punct]--> 's (AUX)`
3680	`maybe (ADV) --[advmod]--> 's (AUX)`
3681	`, (PUNCT) --[punct]--> is (AUX)`
3682	`Mike (PROPN) --[compound]--> Bloomberg (PROPN)`
3683	`Bloomberg (PROPN) --[nsubj]--> is (AUX)`
3684	`is (AUX) --[ccomp]--> think (VERB)`
3685	`this (DET) --[det]--> foil (NOUN)`
3686	`perfect (ADJ) --[amod]--> foil (NOUN)`
3687	`foil (NOUN) --[attr]--> is (AUX)`
3688	`for (ADP) --[prep]--> foil (NOUN)`
3689	`her (PRON) --[poss]--> message (NOUN)`
3690	`message (NOUN) --[pobj]--> for (ADP)`
3691	`on (ADP) --[prep]--> message (NOUN)`
3692	`income (NOUN) --[compound]--> inequality (NOUN)`
3693	`inequality (NOUN) --[pobj]--> on (ADP)`
3694	`- (PUNCT) --[punct]--> is (AUX)`
3695	`is (AUX) --[dep]--> is (AUX)`
3696	`, (PUNCT) --[punct]--> is (AUX)`
3697	`does (AUX) --[aux]--> direct (VERB)`
3698	`she (PRON) --[nsubj]--> direct (VERB)`
3699	`finally (ADV) --[advmod]--> direct (VERB)`
3700	`direct (VERB) --[conj]--> is (AUX)`
3701	`her (PRON) --[poss]--> fire (NOUN)`
3702	`fire (NOUN) --[dobj]--> direct (VERB)`
3703	`at (ADP) --[prep]--> direct (VERB)`
3704	`Bernie (PROPN) --[compound]--> Sanders (PROPN)`
3705	`Sanders (PROPN) --[pobj]--> at (ADP)`
3706	`? (PUNCT) --[punct]--> think (VERB)`
3707
3708	`Sentence 3: They certainly have some overlapping support amongst progressive voters.`
3709	`They (PRON) --[nsubj]--> have (VERB)`
3710	`certainly (ADV) --[advmod]--> have (VERB)`
3711	`have (VERB) --[ROOT]--> have (VERB)`
3712	`some (DET) --[det]--> support (NOUN)`
3713	`overlapping (ADJ) --[amod]--> support (NOUN)`
3714	`support (NOUN) --[dobj]--> have (VERB)`
3715	`amongst (ADP) --[prep]--> support (NOUN)`
3716	`progressive (ADJ) --[amod]--> voters (NOUN)`
3717	`voters (NOUN) --[pobj]--> amongst (ADP)`
3718	`. (PUNCT) --[punct]--> have (VERB)`
3719
3720	`Sentence 4: But, you know, he is the front-runner.`
3721	`But (CCONJ) --[cc]--> is (AUX)`
3722	`, (PUNCT) --[punct]--> is (AUX)`
3723	`you (PRON) --[nsubj]--> know (VERB)`
3724	`know (VERB) --[parataxis]--> is (AUX)`
3725	`, (PUNCT) --[punct]--> is (AUX)`
3726	`he (PRON) --[nsubj]--> is (AUX)`
3727	`is (AUX) --[ROOT]--> is (AUX)`
3728	`the (DET) --[det]--> runner (NOUN)`
3729	`front (ADJ) --[amod]--> runner (NOUN)`
3730	`- (PUNCT) --[punct]--> runner (NOUN)`
3731	`runner (NOUN) --[attr]--> is (AUX)`
3732	`. (PUNCT) --[punct]--> is (AUX)`
3733
3734	`Sentence 5: And in the last debate, it did not seem like his rival saw him that way.`
3735	`And (CCONJ) --[cc]--> seem (VERB)`
3736	`in (ADP) --[prep]--> seem (VERB)`
3737	`the (DET) --[det]--> debate (NOUN)`
3738	`last (ADJ) --[amod]--> debate (NOUN)`
3739	`debate (NOUN) --[pobj]--> in (ADP)`
3740	`, (PUNCT) --[punct]--> seem (VERB)`
3741	`it (PRON) --[nsubj]--> seem (VERB)`
3742	`did (AUX) --[aux]--> seem (VERB)`
3743	`not (PART) --[neg]--> seem (VERB)`
3744	`seem (VERB) --[ROOT]--> seem (VERB)`
3745	`like (SCONJ) --[mark]--> saw (VERB)`
3746	`his (PRON) --[poss]--> rival (NOUN)`
3747	`rival (NOUN) --[nsubj]--> saw (VERB)`
3748	`saw (VERB) --[advcl]--> seem (VERB)`
3749	`him (PRON) --[dobj]--> saw (VERB)`
3750	`that (DET) --[det]--> way (NOUN)`
3751	`way (NOUN) --[npadvmod]--> saw (VERB)`
3752	`. (PUNCT) --[punct]--> seem (VERB)`
3753
3754	`Total sentences: 5`
3755
3756	`RAKE Keyphrase Relationships:`
3757	`noun phrase: "the front-runner" contains [runner], [front]`
3758	`Total relationships found: 1`
3759
3760	`YAKE Keyphrase Relationships:`
3761	`noun phrase: "Mike Bloomberg" contains [mike bloomberg], [mike], [bloomberg]`
3762	`noun phrase: "this perfect foil" contains [perfect foil], [perfect]`
3763	`noun phrase: "income inequality" contains [income inequality], [inequality]`
3764	`noun phrase: "Bernie Sanders" contains [bernie sanders], [sanders], [bernie]`
3765	`Total relationships found: 4`
3766
3767	`Timing Comparison:`
3768	`RAKE: 0.0000sec`
3769	`YAKE: 0.0218sec`
3770	`KeyBERT: 0.0393sec`
3771	`Dependencies: 0.0000sec`
3772	`Fastest: RAKE`
3773
3774	`================================================================================`
3775	`Message 27:`
3776	`LIONEL BOYCE: I'm always, like, attracted to bigger personalities. My mom - she's always kind of been that way - very high-energy, high-strung, just exuberant. And my dad's kind of the opposite, where he's probably closer to how you see what you see me in my performance - where he's very calm, quiet. He's very, like, sweet-natured and gentle.`
3777	`--------------------------------------------------------------------------------`
3778	`RAKE Keywords:`
3779	`- probably closer (score: 4.00) → probably close`
3780	`- lionel boyce (score: 4.00) → LIONEL BOYCE`
3781	`- bigger personalities (score: 4.00) → big personality`
3782	`- always kind (score: 3.00)`
3783	`- kind (score: 1.50)`
3784	`- always (score: 1.50)`
3785	`- way (score: 1.00)`
3786	`- sweet (score: 1.00)`
3787	`- strung (score: 1.00) → string`
3788	`- see (score: 1.00)`
3789	`- see (score: 1.00)`
3790	`- quiet (score: 1.00)`
3791	`- performance (score: 1.00)`
3792	`- opposite (score: 1.00)`
3793	`- natured (score: 1.00)`
3794	`- mom (score: 1.00)`
3795	`- like (score: 1.00)`
3796	`- like (score: 1.00)`
3797	`- high (score: 1.00)`
3798	`- high (score: 1.00)`
3799	`- gentle (score: 1.00)`
3800	`- exuberant (score: 1.00)`
3801	`- energy (score: 1.00)`
3802	`- dad (score: 1.00)`
3803	`- calm (score: 1.00)`
3804	`- attracted (score: 1.00) → attract`
3805	`Total keywords: 26 extracted in 0.0158 seconds`
3806
3807	`YAKE Keywords:`
3808	`- LIONEL BOYCE (score: 0.00) → LIONEL BOYCE`
3809	`- attracted to bigger (score: 0.03) → attract to big`
3810	`- bigger personalities (score: 0.03) → big personality`
3811	`- LIONEL (score: 0.07) → LIONEL`
3812	`- BOYCE (score: 0.07) → BOYCE`
3813	`- attracted (score: 0.14) → attract`
3814	`- personalities (score: 0.14) → personality`
3815	`- bigger (score: 0.20) → big`
3816	`- kind (score: 0.22)`
3817	`- high-strung (score: 0.24)`
3818	`- quiet (score: 0.32)`
3819	`- dad kind (score: 0.33)`
3820	`- sweet-natured and gentle (score: 0.34)`
3821	`- mom (score: 0.36)`
3822	`- high-energy (score: 0.36)`
3823	`- exuberant (score: 0.36)`
3824	`- opposite (score: 0.45)`
3825	`- performance (score: 0.45)`
3826	`- calm (score: 0.45)`
3827	`- sweet-natured (score: 0.50)`
3828	`Total keywords: 20 extracted in 0.0158 seconds`
3829
3830	`KeyBERT Keywords:`
3831	`- lionel boyce like (score: 0.68)`
3832	`- lionel boyce (score: 0.64)`
3833	`- lionel (score: 0.56)`
3834	`- attracted bigger personalities (score: 0.52)`
3835	`- boyce like attracted (score: 0.48)`
3836	`- bigger personalities mom (score: 0.46)`
3837	`- attracted bigger (score: 0.45)`
3838	`- bigger personalities (score: 0.43)`
3839	`- like attracted bigger (score: 0.40)`
3840	`- personalities mom kind (score: 0.39)`
3841	`- personalities mom (score: 0.39)`
3842	`- exuberant dad kind (score: 0.38)`
3843	`- boyce like (score: 0.37)`
3844	`- exuberant dad (score: 0.37)`
3845	`- personalities (score: 0.35)`
3846	`- boyce (score: 0.34)`
3847	`- dad kind (score: 0.34)`
3848	`- attracted (score: 0.33)`
3849	`- dad kind opposite (score: 0.32)`
3850	`- like attracted (score: 0.32)`
3851	`Total keywords: 20 extracted in 0.0516 seconds`
3852
3853	`Dependency Relations (extracted in 0.0159sec):`
3854
3855	`Sentence 1: LIONEL BOYCE:`
3856	`LIONEL (NOUN) --[compound]--> BOYCE (PROPN)`
3857	`BOYCE (PROPN) --[ROOT]--> BOYCE (PROPN)`
3858	`: (PUNCT) --[punct]--> BOYCE (PROPN)`
3859
3860	`Sentence 2: I'm always, like, attracted to bigger personalities.`
3861	`I (PRON) --[nsubj]--> 'm (AUX)`
3862	`'m (AUX) --[ROOT]--> 'm (AUX)`
3863	`always (ADV) --[advmod]--> 'm (AUX)`
3864	`, (PUNCT) --[punct]--> 'm (AUX)`
3865	`like (INTJ) --[intj]--> attracted (VERB)`
3866	`, (PUNCT) --[punct]--> attracted (VERB)`
3867	`attracted (VERB) --[advcl]--> 'm (AUX)`
3868	`to (ADP) --[prep]--> attracted (VERB)`
3869	`bigger (ADJ) --[amod]--> personalities (NOUN)`
3870	`personalities (NOUN) --[pobj]--> to (ADP)`
3871	`. (PUNCT) --[punct]--> 'm (AUX)`
3872
3873	`Sentence 3: My mom - she's always kind of been that way - very high-energy, high-strung, just exuberant.`
3874	`My (PRON) --[poss]--> mom (NOUN)`
3875	`mom (NOUN) --[npadvmod]--> 's (AUX)`
3876	`- (PUNCT) --[punct]--> 's (AUX)`
3877	`she (PRON) --[nsubj]--> 's (AUX)`
3878	`'s (AUX) --[aux]--> been (AUX)`
3879	`always (ADV) --[advmod]--> 's (AUX)`
3880	`kind (ADV) --[advmod]--> of (ADV)`
3881	`of (ADV) --[advmod]--> been (AUX)`
3882	`been (AUX) --[ROOT]--> been (AUX)`
3883	`that (DET) --[det]--> way (NOUN)`
3884	`way (NOUN) --[attr]--> been (AUX)`
3885	`- (PUNCT) --[punct]--> way (NOUN)`
3886	`very (ADV) --[advmod]--> energy (NOUN)`
3887	`high (ADJ) --[amod]--> energy (NOUN)`
3888	`- (PUNCT) --[punct]--> energy (NOUN)`
3889	`energy (NOUN) --[appos]--> way (NOUN)`
3890	`, (PUNCT) --[punct]--> energy (NOUN)`
3891	`high (ADJ) --[amod]--> strung (VERB)`
3892	`- (PUNCT) --[punct]--> strung (VERB)`
3893	`strung (VERB) --[amod]--> exuberant (ADJ)`
3894	`, (PUNCT) --[punct]--> strung (VERB)`
3895	`just (ADV) --[advmod]--> exuberant (ADJ)`
3896	`exuberant (ADJ) --[acomp]--> been (AUX)`
3897	`. (PUNCT) --[punct]--> been (AUX)`
3898
3899	`Sentence 4: And my dad's kind of the opposite, where he's probably closer to how you see what you see me in my performance - where he's very calm, quiet.`
3900	`And (CCONJ) --[cc]--> kind (NOUN)`
3901	`my (PRON) --[poss]--> dad (NOUN)`
3902	`dad (NOUN) --[poss]--> kind (NOUN)`
3903	`'s (PART) --[case]--> dad (NOUN)`
3904	`kind (NOUN) --[ROOT]--> kind (NOUN)`
3905	`of (ADP) --[prep]--> kind (NOUN)`
3906	`the (DET) --[det]--> opposite (NOUN)`
3907	`opposite (NOUN) --[pobj]--> of (ADP)`
3908	`, (PUNCT) --[punct]--> opposite (NOUN)`
3909	`where (SCONJ) --[advmod]--> 's (AUX)`
3910	`he (PRON) --[nsubj]--> 's (AUX)`
3911	`'s (AUX) --[relcl]--> opposite (NOUN)`
3912	`probably (ADV) --[advmod]--> 's (AUX)`
3913	`closer (ADJ) --[acomp]--> 's (AUX)`
3914	`to (ADP) --[prep]--> closer (ADJ)`
3915	`how (SCONJ) --[advmod]--> see (VERB)`
3916	`you (PRON) --[nsubj]--> see (VERB)`
3917	`see (VERB) --[pcomp]--> to (ADP)`
3918	`what (PRON) --[dobj]--> see (VERB)`
3919	`you (PRON) --[nsubj]--> see (VERB)`
3920	`see (VERB) --[ccomp]--> see (VERB)`
3921	`me (PRON) --[dobj]--> see (VERB)`
3922	`in (ADP) --[prep]--> see (VERB)`
3923	`my (PRON) --[poss]--> performance (NOUN)`
3924	`performance (NOUN) --[pobj]--> in (ADP)`
3925	`- (PUNCT) --[punct]--> see (VERB)`
3926	`where (SCONJ) --[advmod]--> 's (AUX)`
3927	`he (PRON) --[nsubj]--> 's (AUX)`
3928	`'s (AUX) --[ccomp]--> see (VERB)`
3929	`very (ADV) --[advmod]--> calm (ADJ)`
3930	`calm (ADJ) --[acomp]--> 's (AUX)`
3931	`, (PUNCT) --[punct]--> quiet (ADJ)`
3932	`quiet (ADJ) --[ccomp]--> see (VERB)`
3933	`. (PUNCT) --[punct]--> kind (NOUN)`
3934
3935	`Sentence 5: He's very, like, sweet-natured and gentle.`
3936	`He (PRON) --[nsubj]--> 's (AUX)`
3937	`'s (AUX) --[ROOT]--> 's (AUX)`
3938	`very (ADV) --[advmod]--> natured (ADJ)`
3939	`, (PUNCT) --[punct]--> natured (ADJ)`
3940	`like (ADP) --[intj]--> natured (ADJ)`
3941	`, (PUNCT) --[punct]--> like (ADP)`
3942	`sweet (ADJ) --[amod]--> natured (ADJ)`
3943	`- (PUNCT) --[punct]--> natured (ADJ)`
3944	`natured (ADJ) --[acomp]--> 's (AUX)`
3945	`and (CCONJ) --[cc]--> natured (ADJ)`
3946	`gentle (ADJ) --[conj]--> natured (ADJ)`
3947	`. (PUNCT) --[punct]--> 's (AUX)`
3948
3949	`Total sentences: 5`
3950
3951	`RAKE Keyphrase Relationships:`
3952	`noun phrase: "very high-energy" contains [high], [high], [energy]`
3953	`noun phrase: "And my dad's kind" contains [kind], [dad]`
3954	`verb phrase: "see how" contains [see], [see]`
3955	`verb phrase: "see what me in" contains [see], [see]`
3956	`Total relationships found: 4`
3957
3958	`YAKE Keyphrase Relationships:`
3959	`noun phrase: "LIONEL BOYCE" contains [lionel boyce], [lionel], [boyce]`
3960	`noun phrase: "bigger personalities" contains [bigger personalities], [personalities], [bigger]`
3961	`Total relationships found: 2`
3962
3963	`Timing Comparison:`
3964	`RAKE: 0.0158sec`
3965	`YAKE: 0.0158sec`
3966	`KeyBERT: 0.0516sec`
3967	`Dependencies: 0.0159sec`
3968	`Fastest: YAKE`
3969
3970	`================================================================================`
3971	`Message 28:`
3972	`CAHILL: And that's really meant sending much larger volumes to India and to China, to Turkey and a handful of other countries in Asia. But they've had to accept lower prices as a result.`
3973	`--------------------------------------------------------------------------------`
3974	`RAKE Keywords:`
3975	`- accept lower prices (score: 9.00) → accept low price`
3976	`- turkey (score: 1.00) → Turkey`
3977	`- result (score: 1.00)`
3978	`- india (score: 1.00) → India`
3979	`- handful (score: 1.00)`
3980	`- countries (score: 1.00) → country`
3981	`- china (score: 1.00) → China`
3982	`- cahill (score: 1.00) → CAHILL`
3983	`- asia (score: 1.00) → Asia`
3984	`Total keywords: 9 extracted in 0.0000 seconds`
3985
3986	`YAKE Keywords:`
3987	`- countries in Asia (score: 0.01) → country in Asia`
3988	`- volumes to India (score: 0.01) → volume to India`
3989	`- meant sending (score: 0.02) → mean send`
3990	`- sending much larger (score: 0.02) → send much large`
3991	`- larger volumes (score: 0.02) → large volume`
3992	`- CAHILL (score: 0.04) → CAHILL`
3993	`- accept lower prices (score: 0.05) → accept low price`
3994	`- China (score: 0.05) → China`
3995	`- Asia (score: 0.05) → Asia`
3996	`- India (score: 0.07) → India`
3997	`- Turkey (score: 0.07) → Turkey`
3998	`- accept lower (score: 0.12) → accept low`
3999	`- lower prices (score: 0.12) → low price`
4000	`- meant (score: 0.12) → mean`
4001	`- sending (score: 0.12) → send`
4002	`- larger (score: 0.12) → large`
4003	`- volumes (score: 0.12) → volume`
4004	`- handful (score: 0.12)`
4005	`- countries (score: 0.12) → country`
4006	`- result (score: 0.25)`
4007	`Total keywords: 20 extracted in 0.0112 seconds`
4008
4009	`KeyBERT Keywords:`
4010	`- volumes india china (score: 0.53)`
4011	`- india china turkey (score: 0.47)`
4012	`- larger volumes india (score: 0.46)`
4013	`- lower prices result (score: 0.43)`
4014	`- india china (score: 0.43)`
4015	`- accept lower prices (score: 0.43)`
4016	`- lower prices (score: 0.42)`
4017	`- volumes india (score: 0.40)`
4018	`- china turkey handful (score: 0.39)`
4019	`- asia ve accept (score: 0.37)`
4020	`- china turkey (score: 0.36)`
4021	`- prices result (score: 0.36)`
4022	`- handful countries asia (score: 0.35)`
4023	`- asia (score: 0.35)`
4024	`- cahill really (score: 0.35)`
4025	`- turkey handful countries (score: 0.34)`
4026	`- prices (score: 0.33)`
4027	`- cahill really meant (score: 0.32)`
4028	`- cahill (score: 0.32)`
4029	`- countries asia (score: 0.32)`
4030	`Total keywords: 20 extracted in 0.0315 seconds`
4031
4032	`Dependency Relations (extracted in 0.0120sec):`
4033
4034	`Sentence 1: CAHILL:`
4035	`CAHILL (PROPN) --[ROOT]--> CAHILL (PROPN)`
4036	`: (PUNCT) --[punct]--> CAHILL (PROPN)`
4037
4038	`Sentence 2: And that's really meant sending much larger volumes to India and to China, to Turkey and a handful of other countries in Asia.`
4039	`And (CCONJ) --[cc]--> meant (VERB)`
4040	`that (PRON) --[nsubjpass]--> meant (VERB)`
4041	`'s (AUX) --[auxpass]--> meant (VERB)`
4042	`really (ADV) --[advmod]--> meant (VERB)`
4043	`meant (VERB) --[ROOT]--> meant (VERB)`
4044	`sending (VERB) --[xcomp]--> meant (VERB)`
4045	`much (ADV) --[advmod]--> larger (ADJ)`
4046	`larger (ADJ) --[amod]--> volumes (NOUN)`
4047	`volumes (NOUN) --[dobj]--> sending (VERB)`
4048	`to (ADP) --[prep]--> sending (VERB)`
4049	`India (PROPN) --[pobj]--> to (ADP)`
4050	`and (CCONJ) --[cc]--> to (ADP)`
4051	`to (ADP) --[conj]--> to (ADP)`
4052	`China (PROPN) --[pobj]--> to (ADP)`
4053	`, (PUNCT) --[punct]--> to (ADP)`
4054	`to (ADP) --[prep]--> sending (VERB)`
4055	`Turkey (PROPN) --[pobj]--> to (ADP)`
4056	`and (CCONJ) --[cc]--> Turkey (PROPN)`
4057	`a (DET) --[det]--> handful (NOUN)`
4058	`handful (NOUN) --[conj]--> Turkey (PROPN)`
4059	`of (ADP) --[prep]--> handful (NOUN)`
4060	`other (ADJ) --[amod]--> countries (NOUN)`
4061	`countries (NOUN) --[pobj]--> of (ADP)`
4062	`in (ADP) --[prep]--> countries (NOUN)`
4063	`Asia (PROPN) --[pobj]--> in (ADP)`
4064	`. (PUNCT) --[punct]--> meant (VERB)`
4065
4066	`Sentence 3: But they've had to accept lower prices as a result.`
4067	`But (CCONJ) --[cc]--> had (VERB)`
4068	`they (PRON) --[nsubj]--> had (VERB)`
4069	`'ve (AUX) --[aux]--> had (VERB)`
4070	`had (VERB) --[ROOT]--> had (VERB)`
4071	`to (PART) --[aux]--> accept (VERB)`
4072	`accept (VERB) --[xcomp]--> had (VERB)`
4073	`lower (ADJ) --[amod]--> prices (NOUN)`
4074	`prices (NOUN) --[dobj]--> accept (VERB)`
4075	`as (ADP) --[prep]--> accept (VERB)`
4076	`a (DET) --[det]--> result (NOUN)`
4077	`result (NOUN) --[pobj]--> as (ADP)`
4078	`. (PUNCT) --[punct]--> had (VERB)`
4079
4080	`Total sentences: 3`
4081
4082	`RAKE Keyphrase Relationships:`
4083	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
4084
4085	`YAKE Keyphrase Relationships:`
4086	`noun phrase: "much larger volumes" contains [larger volumes], [larger], [volumes]`
4087	`verb phrase: "sending volumes to to" contains [sending], [volumes]`
4088	`Total relationships found: 2`
4089
4090	`Timing Comparison:`
4091	`RAKE: 0.0000sec`
4092	`YAKE: 0.0112sec`
4093	`KeyBERT: 0.0315sec`
4094	`Dependencies: 0.0120sec`
4095	`Fastest: RAKE`
4096
4097	`================================================================================`
4098	`Message 29:`
4099	`CORNISH: So you're, like, referring to everyone, right? We get the layout, and your biological father passed away, is that right?K.`
4100	`--------------------------------------------------------------------------------`
4101	`RAKE Keywords:`
4102	`- right (score: 1.00)`
4103	`- right (score: 1.00)`
4104	`- referring (score: 1.00) → refer`
4105	`- like (score: 1.00)`
4106	`- layout (score: 1.00)`
4107	`- k (score: 1.00)`
4108	`- get (score: 1.00)`
4109	`- everyone (score: 1.00)`
4110	`- cornish (score: 1.00) → CORNISH`
4111	`Total keywords: 9 extracted in 0.0000 seconds`
4112
4113	`YAKE Keywords:`
4114	`- CORNISH (score: 0.04) → CORNISH`
4115	`- referring (score: 0.12) → refer`
4116	`- biological father passed (score: 0.18) → biological father pass`
4117	`- biological father (score: 0.28)`
4118	`- father passed (score: 0.28) → father pass`
4119	`- layout (score: 0.33)`
4120	`- biological (score: 0.47)`
4121	`- father (score: 0.47)`
4122	`- passed (score: 0.47) → pass`
4123	`Total keywords: 9 extracted in 0.0037 seconds`
4124
4125	`KeyBERT Keywords:`
4126	`- cornish like referring (score: 0.64)`
4127	`- cornish (score: 0.52)`
4128	`- cornish like (score: 0.52)`
4129	`- layout biological father (score: 0.45)`
4130	`- referring right layout (score: 0.44)`
4131	`- biological father passed (score: 0.36)`
4132	`- biological father (score: 0.36)`
4133	`- like referring right (score: 0.34)`
4134	`- referring right (score: 0.34)`
4135	`- father passed away (score: 0.33)`
4136	`- like referring (score: 0.32)`
4137	`- father (score: 0.32)`
4138	`- father passed (score: 0.32)`
4139	`- referring (score: 0.32)`
4140	`- right layout biological (score: 0.31)`
4141	`- layout biological (score: 0.29)`
4142	`- right layout (score: 0.29)`
4143	`- passed away right (score: 0.29)`
4144	`- layout (score: 0.28)`
4145	`- passed away (score: 0.26)`
4146	`Total keywords: 20 extracted in 0.0160 seconds`
4147
4148	`Dependency Relations (extracted in 0.0140sec):`
4149
4150	`Sentence 1: CORNISH: So you're, like, referring to everyone, right?`
4151	`CORNISH (VERB) --[ROOT]--> CORNISH (VERB)`
4152	`: (PUNCT) --[punct]--> right (ADJ)`
4153	`So (ADV) --[advmod]--> right (ADJ)`
4154	`you (PRON) --[nsubj]--> 're (AUX)`
4155	`'re (AUX) --[ccomp]--> right (ADJ)`
4156	`, (PUNCT) --[punct]--> 're (AUX)`
4157	`like (INTJ) --[prep]--> 're (AUX)`
4158	`, (PUNCT) --[punct]--> 're (AUX)`
4159	`referring (VERB) --[advcl]--> 're (AUX)`
4160	`to (ADP) --[prep]--> referring (VERB)`
4161	`everyone (PRON) --[pobj]--> to (ADP)`
4162	`, (PUNCT) --[punct]--> right (ADJ)`
4163	`right (ADJ) --[intj]--> CORNISH (VERB)`
4164	`? (PUNCT) --[punct]--> right (ADJ)`
4165
4166	`Sentence 2: We get the layout, and your biological father passed away, is that right?K.`
4167	`We (PRON) --[nsubj]--> get (VERB)`
4168	`get (VERB) --[ccomp]--> is (AUX)`
4169	`the (DET) --[det]--> layout (NOUN)`
4170	`layout (NOUN) --[dobj]--> get (VERB)`
4171	`, (PUNCT) --[punct]--> get (VERB)`
4172	`and (CCONJ) --[cc]--> get (VERB)`
4173	`your (PRON) --[poss]--> father (NOUN)`
4174	`biological (ADJ) --[amod]--> father (NOUN)`
4175	`father (NOUN) --[nsubj]--> passed (VERB)`
4176	`passed (VERB) --[conj]--> get (VERB)`
4177	`away (ADP) --[prt]--> passed (VERB)`
4178	`, (PUNCT) --[punct]--> is (AUX)`
4179	`is (AUX) --[ROOT]--> is (AUX)`
4180	`that (SCONJ) --[nsubj]--> is (AUX)`
4181	`right?K. (NOUN) --[attr]--> is (AUX)`
4182
4183	`Total sentences: 2`
4184
4185	`RAKE Keyphrase Relationships:`
4186	`noun phrase: "right?K." contains [right], [right], [k]`
4187	`verb phrase: "get layout" contains [layout], [get]`
4188	`Total relationships found: 2`
4189
4190	`YAKE Keyphrase Relationships:`
4191	`noun phrase: "your biological father" contains [biological father], [biological], [father]`
4192	`Total relationships found: 1`
4193
4194	`Timing Comparison:`
4195	`RAKE: 0.0000sec`
4196	`YAKE: 0.0037sec`
4197	`KeyBERT: 0.0160sec`
4198	`Dependencies: 0.0140sec`
4199	`Fastest: RAKE`
4200
4201	`================================================================================`
4202	`Message 30:`
4203	`KELLY: I'll open with the basic question. Does the former president have your support for 2024?`
4204	`--------------------------------------------------------------------------------`
4205	`RAKE Keywords:`
4206	`- former president (score: 4.00)`
4207	`- basic question (score: 4.00)`
4208	`- support (score: 1.00)`
4209	`- open (score: 1.00)`
4210	`- kelly (score: 1.00) → KELLY`
4211	`- 2024 (score: 1.00)`
4212	`Total keywords: 6 extracted in 0.0000 seconds`
4213
4214	`YAKE Keywords:`
4215	`- basic question (score: 0.03)`
4216	`- KELLY (score: 0.04) → KELLY`
4217	`- question (score: 0.12)`
4218	`- open (score: 0.20)`
4219	`- basic (score: 0.20)`
4220	`- president (score: 0.47)`
4221	`- support (score: 0.47)`
4222	`Total keywords: 7 extracted in 0.0000 seconds`
4223
4224	`KeyBERT Keywords:`
4225	`- president support 2024 (score: 0.82)`
4226	`- support 2024 (score: 0.67)`
4227	`- does president support (score: 0.63)`
4228	`- president support (score: 0.60)`
4229	`- 2024 (score: 0.57)`
4230	`- does president (score: 0.47)`
4231	`- question does president (score: 0.47)`
4232	`- president (score: 0.35)`
4233	`- support (score: 0.30)`
4234	`- kelly ll open (score: 0.27)`
4235	`- kelly ll (score: 0.23)`
4236	`- question does (score: 0.22)`
4237	`- does (score: 0.21)`
4238	`- kelly (score: 0.21)`
4239	`- basic question does (score: 0.20)`
4240	`- question (score: 0.15)`
4241	`- open basic question (score: 0.14)`
4242	`- ll (score: 0.10)`
4243	`- open (score: 0.09)`
4244	`- basic question (score: 0.09)`
4245	`Total keywords: 20 extracted in 0.0315 seconds`
4246
4247	`Dependency Relations (extracted in 0.0040sec):`
4248
4249	`Sentence 1: KELLY:`
4250	`KELLY (PROPN) --[ROOT]--> KELLY (PROPN)`
4251	`: (PUNCT) --[punct]--> KELLY (PROPN)`
4252
4253	`Sentence 2: I'll open with the basic question.`
4254	`I (PRON) --[nsubj]--> open (VERB)`
4255	`'ll (AUX) --[aux]--> open (VERB)`
4256	`open (VERB) --[ROOT]--> open (VERB)`
4257	`with (ADP) --[prep]--> open (VERB)`
4258	`the (DET) --[det]--> question (NOUN)`
4259	`basic (ADJ) --[amod]--> question (NOUN)`
4260	`question (NOUN) --[pobj]--> with (ADP)`
4261	`. (PUNCT) --[punct]--> open (VERB)`
4262
4263	`Sentence 3: Does the former president have your support for 2024?`
4264	`Does (AUX) --[aux]--> have (VERB)`
4265	`the (DET) --[det]--> president (NOUN)`
4266	`former (ADJ) --[amod]--> president (NOUN)`
4267	`president (NOUN) --[nsubj]--> have (VERB)`
4268	`have (VERB) --[ROOT]--> have (VERB)`
4269	`your (PRON) --[poss]--> support (NOUN)`
4270	`support (NOUN) --[dobj]--> have (VERB)`
4271	`for (ADP) --[prep]--> have (VERB)`
4272	`2024 (NUM) --[pobj]--> for (ADP)`
4273	`? (PUNCT) --[punct]--> have (VERB)`
4274
4275	`Total sentences: 3`
4276
4277	`RAKE Keyphrase Relationships:`
4278	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
4279
4280	`YAKE Keyphrase Relationships:`
4281	`noun phrase: "the basic question" contains [basic question], [question], [basic]`
4282	`Total relationships found: 1`
4283
4284	`Timing Comparison:`
4285	`RAKE: 0.0000sec`
4286	`YAKE: 0.0000sec`
4287	`KeyBERT: 0.0315sec`
4288	`Dependencies: 0.0040sec`
4289	`Fastest: RAKE`
4290
4291	`================================================================================`
4292	`Message 31:`
4293	`UNIDENTIFIED GROUP: (Singing) Turn me round.`
4294	`--------------------------------------------------------------------------------`
4295	`RAKE Keywords:`
4296	`- unidentified group (score: 4.00) → UNIDENTIFIED GROUP`
4297	`- turn (score: 1.00)`
4298	`- singing (score: 1.00)`
4299	`- round (score: 1.00)`
4300	`Total keywords: 4 extracted in 0.0000 seconds`
4301
4302	`YAKE Keywords:`
4303	`- UNIDENTIFIED GROUP (score: 0.01) → UNIDENTIFIED GROUP`
4304	`- Turn me round (score: 0.01) → turn I round`
4305	`- Singing (score: 0.03)`
4306	`- UNIDENTIFIED (score: 0.09) → UNIDENTIFIED`
4307	`- GROUP (score: 0.09) → GROUP`
4308	`- Turn (score: 0.09)`
4309	`- round (score: 0.16)`
4310	`Total keywords: 7 extracted in 0.0000 seconds`
4311
4312	`KeyBERT Keywords:`
4313	`- unidentified group singing (score: 0.80)`
4314	`- group singing turn (score: 0.77)`
4315	`- singing turn round (score: 0.72)`
4316	`- group singing (score: 0.69)`
4317	`- singing turn (score: 0.65)`
4318	`- unidentified group (score: 0.63)`
4319	`- turn round (score: 0.54)`
4320	`- singing (score: 0.51)`
4321	`- group (score: 0.45)`
4322	`- turn (score: 0.44)`
4323	`- round (score: 0.42)`
4324	`- unidentified (score: 0.37)`
4325	`Total keywords: 12 extracted in 0.0198 seconds`
4326
4327	`Dependency Relations (extracted in 0.0060sec):`
4328
4329	`Sentence 1: UNIDENTIFIED GROUP: (Singing) Turn me round.`
4330	`UNIDENTIFIED (PROPN) --[compound]--> GROUP (PROPN)`
4331	`GROUP (PROPN) --[nsubj]--> Turn (VERB)`
4332	`: (PUNCT) --[punct]--> GROUP (PROPN)`
4333	`( (PUNCT) --[punct]--> Singing (NOUN)`
4334	`Singing (NOUN) --[appos]--> GROUP (PROPN)`
4335	`) (PUNCT) --[punct]--> Singing (NOUN)`
4336	`Turn (VERB) --[ROOT]--> Turn (VERB)`
4337	`me (PRON) --[dobj]--> Turn (VERB)`
4338	`round (ADV) --[advmod]--> Turn (VERB)`
4339	`. (PUNCT) --[punct]--> Turn (VERB)`
4340
4341	`Total sentences: 1`
4342
4343	`RAKE Keyphrase Relationships:`
4344	`verb phrase: "Turn me round" contains [turn], [round]`
4345	`Total relationships found: 1`
4346
4347	`YAKE Keyphrase Relationships:`
4348	`noun phrase: "UNIDENTIFIED GROUP" contains [unidentified group], [unidentified], [group]`
4349	`verb phrase: "Turn me round" contains [turn me round], [turn], [round]`
4350	`Total relationships found: 2`
4351
4352	`Timing Comparison:`
4353	`RAKE: 0.0000sec`
4354	`YAKE: 0.0000sec`
4355	`KeyBERT: 0.0198sec`
4356	`Dependencies: 0.0060sec`
4357	`Fastest: RAKE`
4358
4359	`================================================================================`
4360	`Message 32:`
4361	`MORGAN: Have you had it this bad before?`
4362	`--------------------------------------------------------------------------------`
4363	`RAKE Keywords:`
4364	`- morgan (score: 1.00) → MORGAN`
4365	`- bad (score: 1.00)`
4366	`Total keywords: 2 extracted in 0.0000 seconds`
4367
4368	`YAKE Keywords:`
4369	`- MORGAN (score: 0.03) → MORGAN`
4370	`- bad (score: 0.30)`
4371	`Total keywords: 2 extracted in 0.0000 seconds`
4372
4373	`KeyBERT Keywords:`
4374	`- morgan bad (score: 0.67)`
4375	`- morgan (score: 0.54)`
4376	`- bad (score: 0.26)`
4377	`Total keywords: 3 extracted in 0.0176 seconds`
4378
4379	`Dependency Relations (extracted in 0.0040sec):`
4380
4381	`Sentence 1: MORGAN: Have you had it this bad before?`
4382	`MORGAN (PROPN) --[npadvmod]--> had (VERB)`
4383	`: (PUNCT) --[punct]--> MORGAN (PROPN)`
4384	`Have (AUX) --[aux]--> had (VERB)`
4385	`you (PRON) --[nsubj]--> had (VERB)`
4386	`had (VERB) --[ROOT]--> had (VERB)`
4387	`it (PRON) --[dobj]--> had (VERB)`
4388	`this (DET) --[det]--> bad (ADJ)`
4389	`bad (ADJ) --[ccomp]--> had (VERB)`
4390	`before (ADV) --[advmod]--> had (VERB)`
4391	`? (PUNCT) --[punct]--> had (VERB)`
4392
4393	`Total sentences: 1`
4394
4395	`RAKE Keyphrase Relationships:`
4396	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
4397
4398	`YAKE Keyphrase Relationships:`
4399	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
4400
4401	`Timing Comparison:`
4402	`RAKE: 0.0000sec`
4403	`YAKE: 0.0000sec`
4404	`KeyBERT: 0.0176sec`
4405	`Dependencies: 0.0040sec`
4406	`Fastest: RAKE`
4407
4408	`================================================================================`
4409	`Message 33:`
4410	`JENKINS: (As Erik Blake) This is what matters right here. But end of the day, everything anyone's got, one day it goes.`
4411	`--------------------------------------------------------------------------------`
4412	`RAKE Keywords:`
4413	`- matters right (score: 4.00) → matter right`
4414	`- everything anyone (score: 4.00)`
4415	`- erik blake (score: 4.00) → Erik Blake`
4416	`- one day (score: 3.50)`
4417	`- day (score: 1.50)`
4418	`- jenkins (score: 1.00) → jenkin`
4419	`- got (score: 1.00) → get`
4420	`- goes (score: 1.00) → go`
4421	`- end (score: 1.00)`
4422	`Total keywords: 9 extracted in 0.0000 seconds`
4423
4424	`YAKE Keywords:`
4425	`- Erik Blake (score: 0.01) → Erik Blake`
4426	`- JENKINS (score: 0.04) → jenkin`
4427	`- Blake (score: 0.07) → Blake`
4428	`- Erik (score: 0.11) → Erik`
4429	`- matters (score: 0.25) → matter`
4430	`- day (score: 0.31)`
4431	`- end (score: 0.53)`
4432	`Total keywords: 7 extracted in 0.0017 seconds`
4433
4434	`KeyBERT Keywords:`
4435	`- jenkins (score: 0.66)`
4436	`- jenkins erik blake (score: 0.63)`
4437	`- jenkins erik (score: 0.62)`
4438	`- erik blake matters (score: 0.39)`
4439	`- blake matters right (score: 0.37)`
4440	`- blake matters (score: 0.36)`
4441	`- matters right end (score: 0.34)`
4442	`- erik blake (score: 0.31)`
4443	`- end day got (score: 0.30)`
4444	`- day goes (score: 0.30)`
4445	`- got day goes (score: 0.29)`
4446	`- end day (score: 0.28)`
4447	`- blake (score: 0.28)`
4448	`- erik (score: 0.27)`
4449	`- right end day (score: 0.27)`
4450	`- day got day (score: 0.26)`
4451	`- matters right (score: 0.25)`
4452	`- day got (score: 0.25)`
4453	`- matters (score: 0.23)`
4454	`- goes (score: 0.23)`
4455	`Total keywords: 20 extracted in 0.0255 seconds`
4456
4457	`Dependency Relations (extracted in 0.0098sec):`
4458
4459	`Sentence 1: JENKINS: (As Erik Blake)`
4460	`JENKINS (NOUN) --[ROOT]--> JENKINS (NOUN)`
4461	`: (PUNCT) --[punct]--> JENKINS (NOUN)`
4462	`( (PUNCT) --[punct]--> JENKINS (NOUN)`
4463	`As (ADP) --[prep]--> JENKINS (NOUN)`
4464	`Erik (PROPN) --[compound]--> Blake (PROPN)`
4465	`Blake (PROPN) --[pobj]--> As (ADP)`
4466	`) (PUNCT) --[punct]--> JENKINS (NOUN)`
4467
4468	`Sentence 2: This is what matters right here.`
4469	`This (PRON) --[nsubj]--> is (AUX)`
4470	`is (AUX) --[ROOT]--> is (AUX)`
4471	`what (PRON) --[nsubj]--> matters (VERB)`
4472	`matters (VERB) --[ccomp]--> is (AUX)`
4473	`right (ADV) --[advmod]--> here (ADV)`
4474	`here (ADV) --[advmod]--> matters (VERB)`
4475	`. (PUNCT) --[punct]--> is (AUX)`
4476
4477	`Sentence 3: But end of the day, everything anyone's got, one day it goes.`
4478	`But (CCONJ) --[cc]--> goes (VERB)`
4479	`end (NOUN) --[npadvmod]--> goes (VERB)`
4480	`of (ADP) --[prep]--> end (NOUN)`
4481	`the (DET) --[det]--> day (NOUN)`
4482	`day (NOUN) --[pobj]--> of (ADP)`
4483	`, (PUNCT) --[punct]--> goes (VERB)`
4484	`everything (PRON) --[nsubj]--> goes (VERB)`
4485	`anyone (PRON) --[nsubjpass]--> got (VERB)`
4486	`'s (AUX) --[auxpass]--> got (VERB)`
4487	`got (VERB) --[relcl]--> everything (PRON)`
4488	`, (PUNCT) --[punct]--> everything (PRON)`
4489	`one (NUM) --[nummod]--> day (NOUN)`
4490	`day (NOUN) --[npadvmod]--> goes (VERB)`
4491	`it (PRON) --[nsubj]--> goes (VERB)`
4492	`goes (VERB) --[ROOT]--> goes (VERB)`
4493	`. (PUNCT) --[punct]--> goes (VERB)`
4494
4495	`Total sentences: 3`
4496
4497	`RAKE Keyphrase Relationships:`
4498	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
4499
4500	`YAKE Keyphrase Relationships:`
4501	`noun phrase: "Erik Blake" contains [erik blake], [blake], [erik]`
4502	`Total relationships found: 1`
4503
4504	`Timing Comparison:`
4505	`RAKE: 0.0000sec`
4506	`YAKE: 0.0017sec`
4507	`KeyBERT: 0.0255sec`
4508	`Dependencies: 0.0098sec`
4509	`Fastest: RAKE`
4510
4511	`================================================================================`
4512	`Message 34:`
4513	DREISBACH: It's hard to imagine now, but historians have documented how very early on, the Ku Klux Klan adopted almost absurd rituals. Their costumes and titles like dragons and ghouls and goblins were often seen by white officials as outlandish. They didn't take seriously the stories of attacks by hooded men in white sheets. But eventually newspaper reporters and government investigators started documenting how those hooded men were, in fact, committing murder and other atrocities. They pulled back the Klan hood to see the terrorism and violence it masked.Tom Dreisbach, NPR News.(SOUNDBITE OF ODDISEE SONG, "WANT SOMETHING DONE")
4514	`--------------------------------------------------------------------------------`
4515	`RAKE Keywords:`
4516	`- titles like dragons (score: 9.00) → title like dragon`
4517	`- eventually newspaper reporters (score: 9.00) → eventually newspaper reporter`
4518	`- white sheets (score: 4.00) → white sheet`
4519	`- white officials (score: 4.00) → white official`
4520	`- take seriously (score: 4.00)`
4521	`- pulled back (score: 4.00) → pull back`
4522	`- often seen (score: 4.00) → often see`
4523	`- oddisee song (score: 4.00) → ODDISEE SONG`
4524	`- npr news (score: 4.00)`
4525	`- klan hood (score: 4.00) → Klan hood`
4526	`- hooded men (score: 4.00) → hooded man`
4527	`- hooded men (score: 4.00) → hooded man`
4528	`- committing murder (score: 4.00) → commit murder`
4529	`- tom dreisbach (score: 3.50) → Tom Dreisbach`
4530	`- dreisbach (score: 1.50) → DREISBACH`
4531	`- violence (score: 1.00)`
4532	`- terrorism (score: 1.00)`
4533	`- stories (score: 1.00) → story`
4534	`- soundbite (score: 1.00)`
4535	`- see (score: 1.00)`
4536	`- outlandish (score: 1.00)`
4537	`- masked (score: 1.00) → mask`
4538	`- imagine (score: 1.00)`
4539	`- historians (score: 1.00) → historian`
4540	`- hard (score: 1.00)`
4541	`- goblins (score: 1.00) → goblin`
4542	`- ghouls (score: 1.00) → ghoul`
4543	`- fact (score: 1.00)`
4544	`- early (score: 1.00)`
4545	`- documented (score: 1.00) → document`
4546	`- costumes (score: 1.00) → costume`
4547	`- attacks (score: 1.00) → attack`
4548	`- atrocities (score: 1.00) → atrocity`
4549	`Total keywords: 33 extracted in 0.0000 seconds`
4550
4551	`YAKE Keywords:`
4552	`- Klux Klan adopted (score: 0.00) → Klux Klan adopt`
4553	`- Klux Klan (score: 0.02) → Klux Klan`
4554	`- absurd rituals (score: 0.02) → absurd ritual`
4555	`- hard to imagine (score: 0.02)`
4556	`- historians have documented (score: 0.02) → historian have document`
4557	`- adopted almost absurd (score: 0.02) → adopt almost absurd`
4558	`- Klan adopted (score: 0.04) → Klan adopt`
4559	`- Klux (score: 0.07) → Klux`
4560	`- hooded men (score: 0.09) → hooded man`
4561	`- NPR News. (score: 0.10)`
4562	`- SOUNDBITE OF ODDISEE (score: 0.12)`
4563	`- ODDISEE SONG (score: 0.12) → ODDISEE SONG`
4564	`- DREISBACH (score: 0.12) → DREISBACH`
4565	`- rituals (score: 0.12) → ritual`
4566	`- Klan (score: 0.13) → Klan`
4567	`- officials as outlandish (score: 0.15) → official as outlandish`
4568	`- hard (score: 0.16)`
4569	`- imagine (score: 0.16)`
4570	`- historians (score: 0.16) → historian`
4571	`- documented (score: 0.16) → document`
4572	`Total keywords: 20 extracted in 0.0195 seconds`
4573
4574	`KeyBERT Keywords:`
4575	`- klan hood terrorism (score: 0.66)`
4576	`- ku klux klan (score: 0.65)`
4577	`- klux klan (score: 0.63)`
4578	`- klan hood (score: 0.63)`
4579	`- pulled klan hood (score: 0.61)`
4580	`- atrocities pulled klan (score: 0.60)`
4581	`- klux klan adopted (score: 0.60)`
4582	`- klan adopted absurd (score: 0.59)`
4583	`- klan (score: 0.59)`
4584	`- pulled klan (score: 0.54)`
4585	`- klan adopted (score: 0.54)`
4586	`- hooded men white (score: 0.49)`
4587	`- goblins seen white (score: 0.47)`
4588	`- attacks hooded men (score: 0.47)`
4589	`- stories attacks hooded (score: 0.46)`
4590	`- documenting hooded men (score: 0.44)`
4591	`- hooded men fact (score: 0.44)`
4592	`- hooded men (score: 0.43)`
4593	`- murder atrocities pulled (score: 0.43)`
4594	`- dreisbach npr news (score: 0.43)`
4595	`Total keywords: 20 extracted in 0.1166 seconds`
4596
4597	`Dependency Relations (extracted in 0.0274sec):`
4598
4599	`Sentence 1: DREISBACH: It's hard to imagine now, but historians have documented how very early on, the Ku Klux Klan adopted almost absurd rituals.`
4600	`DREISBACH (PROPN) --[npadvmod]--> 's (AUX)`
4601	`: (PUNCT) --[punct]--> 's (AUX)`
4602	`It (PRON) --[nsubj]--> 's (AUX)`
4603	`'s (AUX) --[ccomp]--> adopted (VERB)`
4604	`hard (ADJ) --[acomp]--> 's (AUX)`
4605	`to (PART) --[aux]--> imagine (VERB)`
4606	`imagine (VERB) --[xcomp]--> 's (AUX)`
4607	`now (ADV) --[advmod]--> imagine (VERB)`
4608	`, (PUNCT) --[punct]--> 's (AUX)`
4609	`but (CCONJ) --[cc]--> 's (AUX)`
4610	`historians (NOUN) --[nsubj]--> documented (VERB)`
4611	`have (AUX) --[aux]--> documented (VERB)`
4612	`documented (VERB) --[conj]--> 's (AUX)`
4613	`how (SCONJ) --[advmod]--> on (ADV)`
4614	`very (ADV) --[advmod]--> early (ADV)`
4615	`early (ADV) --[advmod]--> on (ADV)`
4616	`on (ADV) --[advmod]--> documented (VERB)`
4617	`, (PUNCT) --[punct]--> adopted (VERB)`
4618	`the (DET) --[det]--> Klan (PROPN)`
4619	`Ku (PROPN) --[compound]--> Klan (PROPN)`
4620	`Klux (PROPN) --[compound]--> Klan (PROPN)`
4621	`Klan (PROPN) --[nsubj]--> adopted (VERB)`
4622	`adopted (VERB) --[ROOT]--> adopted (VERB)`
4623	`almost (ADV) --[advmod]--> absurd (ADJ)`
4624	`absurd (ADJ) --[amod]--> rituals (NOUN)`
4625	`rituals (NOUN) --[dobj]--> adopted (VERB)`
4626	`. (PUNCT) --[punct]--> adopted (VERB)`
4627
4628	`Sentence 2: Their costumes and titles like dragons and ghouls and goblins were often seen by white officials as outlandish.`
4629	`Their (PRON) --[poss]--> costumes (NOUN)`
4630	`costumes (NOUN) --[nsubjpass]--> seen (VERB)`
4631	`and (CCONJ) --[cc]--> costumes (NOUN)`
4632	`titles (NOUN) --[conj]--> costumes (NOUN)`
4633	`like (ADP) --[prep]--> costumes (NOUN)`
4634	`dragons (NOUN) --[pobj]--> like (ADP)`
4635	`and (CCONJ) --[cc]--> dragons (NOUN)`
4636	`ghouls (NOUN) --[conj]--> dragons (NOUN)`
4637	`and (CCONJ) --[cc]--> dragons (NOUN)`
4638	`goblins (NOUN) --[conj]--> dragons (NOUN)`
4639	`were (AUX) --[auxpass]--> seen (VERB)`
4640	`often (ADV) --[advmod]--> seen (VERB)`
4641	`seen (VERB) --[ROOT]--> seen (VERB)`
4642	`by (ADP) --[agent]--> seen (VERB)`
4643	`white (ADJ) --[amod]--> officials (NOUN)`
4644	`officials (NOUN) --[pobj]--> by (ADP)`
4645	`as (ADP) --[prep]--> seen (VERB)`
4646	`outlandish (ADJ) --[amod]--> as (ADP)`
4647	`. (PUNCT) --[punct]--> seen (VERB)`
4648
4649	`Sentence 3: They didn't take seriously the stories of attacks by hooded men in white sheets.`
4650	`They (PRON) --[nsubj]--> take (VERB)`
4651	`did (AUX) --[aux]--> take (VERB)`
4652	`n't (PART) --[neg]--> take (VERB)`
4653	`take (VERB) --[ROOT]--> take (VERB)`
4654	`seriously (ADV) --[advmod]--> take (VERB)`
4655	`the (DET) --[det]--> stories (NOUN)`
4656	`stories (NOUN) --[dobj]--> take (VERB)`
4657	`of (ADP) --[prep]--> stories (NOUN)`
4658	`attacks (NOUN) --[pobj]--> of (ADP)`
4659	`by (ADP) --[prep]--> attacks (NOUN)`
4660	`hooded (ADJ) --[amod]--> men (NOUN)`
4661	`men (NOUN) --[pobj]--> by (ADP)`
4662	`in (ADP) --[prep]--> men (NOUN)`
4663	`white (ADJ) --[amod]--> sheets (NOUN)`
4664	`sheets (NOUN) --[pobj]--> in (ADP)`
4665	`. (PUNCT) --[punct]--> take (VERB)`
4666
4667	`Sentence 4: But eventually newspaper reporters and government investigators started documenting how those hooded men were, in fact, committing murder and other atrocities.`
4668	`But (CCONJ) --[cc]--> started (VERB)`
4669	`eventually (ADV) --[advmod]--> newspaper (VERB)`
4670	`newspaper (VERB) --[csubj]--> started (VERB)`
4671	`reporters (NOUN) --[dobj]--> newspaper (VERB)`
4672	`and (CCONJ) --[cc]--> reporters (NOUN)`
4673	`government (NOUN) --[compound]--> investigators (NOUN)`
4674	`investigators (NOUN) --[conj]--> reporters (NOUN)`
4675	`started (VERB) --[ROOT]--> started (VERB)`
4676	`documenting (VERB) --[xcomp]--> started (VERB)`
4677	`how (SCONJ) --[advmod]--> were (AUX)`
4678	`those (DET) --[det]--> men (NOUN)`
4679	`hooded (ADJ) --[amod]--> men (NOUN)`
4680	`men (NOUN) --[nsubj]--> were (AUX)`
4681	`were (AUX) --[ccomp]--> documenting (VERB)`
4682	`, (PUNCT) --[punct]--> were (AUX)`
4683	`in (ADP) --[prep]--> were (AUX)`
4684	`fact (NOUN) --[pobj]--> in (ADP)`
4685	`, (PUNCT) --[punct]--> were (AUX)`
4686	`committing (VERB) --[advcl]--> documenting (VERB)`
4687	`murder (NOUN) --[dobj]--> committing (VERB)`
4688	`and (CCONJ) --[cc]--> murder (NOUN)`
4689	`other (ADJ) --[amod]--> atrocities (NOUN)`
4690	`atrocities (NOUN) --[conj]--> murder (NOUN)`
4691	`. (PUNCT) --[punct]--> started (VERB)`
4692
4693	`Sentence 5: They pulled back the Klan hood to see the terrorism and violence it masked.`
4694	`They (PRON) --[nsubj]--> pulled (VERB)`
4695	`pulled (VERB) --[ROOT]--> pulled (VERB)`
4696	`back (ADV) --[advmod]--> pulled (VERB)`
4697	`the (DET) --[det]--> Klan (PROPN)`
4698	`Klan (PROPN) --[compound]--> hood (NOUN)`
4699	`hood (NOUN) --[dobj]--> pulled (VERB)`
4700	`to (PART) --[aux]--> see (VERB)`
4701	`see (VERB) --[advcl]--> pulled (VERB)`
4702	`the (DET) --[det]--> terrorism (NOUN)`
4703	`terrorism (NOUN) --[dobj]--> see (VERB)`
4704	`and (CCONJ) --[cc]--> terrorism (NOUN)`
4705	`violence (NOUN) --[conj]--> terrorism (NOUN)`
4706	`it (PRON) --[nsubj]--> masked (VERB)`
4707	`masked (VERB) --[relcl]--> terrorism (NOUN)`
4708	`. (PUNCT) --[punct]--> pulled (VERB)`
4709
4710	`Sentence 6: Tom Dreisbach, NPR News.(SOUNDBITE OF ODDISEE SONG, "WANT SOMETHING DONE")`
4711	`Tom (PROPN) --[compound]--> Dreisbach (PROPN)`
4712	`Dreisbach (PROPN) --[nsubj]--> WANT (VERB)`
4713	`, (PUNCT) --[punct]--> Dreisbach (PROPN)`
4714	`NPR (PROPN) --[compound]--> News.(SOUNDBITE (PROPN)`
4715	`News.(SOUNDBITE (PROPN) --[appos]--> Dreisbach (PROPN)`
4716	`OF (ADP) --[prep]--> News.(SOUNDBITE (PROPN)`
4717	`ODDISEE (PROPN) --[compound]--> SONG (PROPN)`
4718	`SONG (PROPN) --[pobj]--> OF (ADP)`
4719	`, (PUNCT) --[punct]--> Dreisbach (PROPN)`
4720	`" (PUNCT) --[punct]--> WANT (VERB)`
4721	`WANT (VERB) --[ROOT]--> WANT (VERB)`
4722	`SOMETHING (PRON) --[nsubj]--> DONE (VERB)`
4723	`DONE (VERB) --[ccomp]--> WANT (VERB)`
4724	`" (PUNCT) --[punct]--> DONE (VERB)`
4725	`) (PUNCT) --[punct]--> WANT (VERB)`
4726
4727	`Total sentences: 6`
4728
4729	`RAKE Keyphrase Relationships:`
4730	`noun phrase: "hooded men" contains [hooded men], [hooded men]`
4731	`noun phrase: "those hooded men" contains [hooded men], [hooded men]`
4732	`noun phrase: "Tom Dreisbach" contains [tom dreisbach], [dreisbach]`
4733	`noun phrase: "NPR News.(SOUNDBITE" contains [npr news], [soundbite]`
4734	`noun phrase: "ODDISEE SONG" contains [oddisee song], [see]`
4735	`verb phrase: "see to terrorism" contains [terrorism], [see]`
4736	`Total relationships found: 6`
4737
4738	`YAKE Keyphrase Relationships:`
4739	`noun phrase: "the Ku Klux Klan" contains [klux klan], [klux], [klan]`
4740	`noun phrase: "almost absurd rituals" contains [absurd rituals], [rituals]`
4741	`Total relationships found: 2`
4742
4743	`Timing Comparison:`
4744	`RAKE: 0.0000sec`
4745	`YAKE: 0.0195sec`
4746	`KeyBERT: 0.1166sec`
4747	`Dependencies: 0.0274sec`
4748	`Fastest: RAKE`
4749
4750	`================================================================================`
4751	`Message 35:`
4752	`ARCUNI: Chris Orrick is with the California Department of Water Resources. He says the dry winter is in stark contrast to last year.`
4753	`--------------------------------------------------------------------------------`
4754	`RAKE Keywords:`
4755	`- water resources (score: 4.00) → Water Resources`
4756	`- stark contrast (score: 4.00)`
4757	`- last year (score: 4.00)`
4758	`- dry winter (score: 4.00)`
4759	`- chris orrick (score: 4.00) → Chris Orrick`
4760	`- california department (score: 4.00) → California Department`
4761	`- says (score: 1.00) → say`
4762	`- arcuni (score: 1.00)`
4763	`Total keywords: 8 extracted in 0.0000 seconds`
4764
4765	`YAKE Keywords:`
4766	`- Chris Orrick (score: 0.01) → Chris Orrick`
4767	`- Water Resources (score: 0.01) → Water Resources`
4768	`- California Department (score: 0.01) → California Department`
4769	`- Department of Water (score: 0.01) → Department of Water`
4770	`- ARCUNI (score: 0.04)`
4771	`- Chris (score: 0.07) → Chris`
4772	`- Resources (score: 0.07) → Resources`
4773	`- Orrick (score: 0.10) → Orrick`
4774	`- California (score: 0.10) → California`
4775	`- Department (score: 0.10) → Department`
4776	`- Water (score: 0.10) → Water`
4777	`- dry winter (score: 0.28)`
4778	`- stark contrast (score: 0.28)`
4779	`- year (score: 0.33)`
4780	`- dry (score: 0.47)`
4781	`- winter (score: 0.47)`
4782	`- stark (score: 0.47)`
4783	`- contrast (score: 0.47)`
4784	`Total keywords: 18 extracted in 0.0040 seconds`
4785
4786	`KeyBERT Keywords:`
4787	`- dry winter stark (score: 0.58)`
4788	`- dry winter (score: 0.56)`
4789	`- arcuni chris orrick (score: 0.55)`
4790	`- says dry winter (score: 0.54)`
4791	`- arcuni chris (score: 0.50)`
4792	`- orrick california department (score: 0.46)`
4793	`- winter stark contrast (score: 0.46)`
4794	`- chris orrick california (score: 0.46)`
4795	`- california department water (score: 0.43)`
4796	`- winter stark (score: 0.43)`
4797	`- orrick california (score: 0.42)`
4798	`- department water resources (score: 0.42)`
4799	`- arcuni (score: 0.41)`
4800	`- winter (score: 0.40)`
4801	`- resources says dry (score: 0.39)`
4802	`- water resources says (score: 0.39)`
4803	`- stark contrast year (score: 0.39)`
4804	`- chris orrick (score: 0.38)`
4805	`- department water (score: 0.36)`
4806	`- water resources (score: 0.34)`
4807	`Total keywords: 20 extracted in 0.0418 seconds`
4808
4809	`Dependency Relations (extracted in 0.0080sec):`
4810
4811	`Sentence 1: ARCUNI: Chris Orrick is with the California Department of Water Resources.`
4812	`ARCUNI (ADP) --[dep]--> is (AUX)`
4813	`: (PUNCT) --[punct]--> ARCUNI (ADP)`
4814	`Chris (PROPN) --[compound]--> Orrick (PROPN)`
4815	`Orrick (PROPN) --[nsubj]--> is (AUX)`
4816	`is (AUX) --[ROOT]--> is (AUX)`
4817	`with (ADP) --[prep]--> is (AUX)`
4818	`the (DET) --[det]--> Department (PROPN)`
4819	`California (PROPN) --[compound]--> Department (PROPN)`
4820	`Department (PROPN) --[pobj]--> with (ADP)`
4821	`of (ADP) --[prep]--> Department (PROPN)`
4822	`Water (PROPN) --[compound]--> Resources (PROPN)`
4823	`Resources (PROPN) --[pobj]--> of (ADP)`
4824	`. (PUNCT) --[punct]--> is (AUX)`
4825
4826	`Sentence 2: He says the dry winter is in stark contrast to last year.`
4827	`He (PRON) --[nsubj]--> says (VERB)`
4828	`says (VERB) --[ROOT]--> says (VERB)`
4829	`the (DET) --[det]--> winter (NOUN)`
4830	`dry (ADJ) --[amod]--> winter (NOUN)`
4831	`winter (NOUN) --[nsubj]--> is (AUX)`
4832	`is (AUX) --[ccomp]--> says (VERB)`
4833	`in (ADP) --[prep]--> is (AUX)`
4834	`stark (ADJ) --[amod]--> contrast (NOUN)`
4835	`contrast (NOUN) --[pobj]--> in (ADP)`
4836	`to (ADP) --[prep]--> contrast (NOUN)`
4837	`last (ADJ) --[amod]--> year (NOUN)`
4838	`year (NOUN) --[pobj]--> to (ADP)`
4839	`. (PUNCT) --[punct]--> says (VERB)`
4840
4841	`Total sentences: 2`
4842
4843	`RAKE Keyphrase Relationships:`
4844	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
4845
4846	`YAKE Keyphrase Relationships:`
4847	`noun phrase: "Chris Orrick" contains [chris orrick], [chris], [orrick]`
4848	`noun phrase: "the California Department" contains [california department], [california], [department]`
4849	`noun phrase: "Water Resources" contains [water resources], [resources], [water]`
4850	`noun phrase: "the dry winter" contains [dry winter], [dry], [winter]`
4851	`noun phrase: "stark contrast" contains [stark contrast], [stark], [contrast]`
4852	`Total relationships found: 5`
4853
4854	`Timing Comparison:`
4855	`RAKE: 0.0000sec`
4856	`YAKE: 0.0040sec`
4857	`KeyBERT: 0.0418sec`
4858	`Dependencies: 0.0080sec`
4859	`Fastest: RAKE`
4860
4861	`================================================================================`
4862	`Message 36:`
4863	`KELLY: Also there was Representative Elissa Slotkin. She's a Democrat from Michigan, used to work at both CIA and the Department of Defense. I asked her whether the intelligence about Russian bounties sounded credible.`
4864	`--------------------------------------------------------------------------------`
4865	`RAKE Keywords:`
4866	`- representative elissa slotkin (score: 9.00) → Representative Elissa Slotkin`
4867	`- work (score: 1.00)`
4868	`- whether (score: 1.00)`
4869	`- used (score: 1.00) → use`
4870	`- michigan (score: 1.00) → Michigan`
4871	`- kelly (score: 1.00) → KELLY`
4872	`- intelligence (score: 1.00)`
4873	`- department (score: 1.00) → Department`
4874	`- democrat (score: 1.00) → Democrat`
4875	`- defense (score: 1.00) → Defense`
4876	`- cia (score: 1.00) → CIA`
4877	`- asked (score: 1.00) → ask`
4878	`- also (score: 1.00)`
4879	`Total keywords: 13 extracted in 0.0000 seconds`
4880
4881	`YAKE Keywords:`
4882	`- Representative Elissa Slotkin (score: 0.00) → Representative Elissa Slotkin`
4883	`- Elissa Slotkin (score: 0.01) → Elissa Slotkin`
4884	`- Representative Elissa (score: 0.01) → Representative Elissa`
4885	`- KELLY (score: 0.04) → KELLY`
4886	`- Democrat from Michigan (score: 0.06) → Democrat from Michigan`
4887	`- Department of Defense (score: 0.06) → Department of Defense`
4888	`- Slotkin (score: 0.07) → Slotkin`
4889	`- Representative (score: 0.10) → Representative`
4890	`- Elissa (score: 0.10) → Elissa`
4891	`- Michigan (score: 0.21) → Michigan`
4892	`- Defense (score: 0.21) → Defense`
4893	`- Russian bounties sounded (score: 0.26) → russian bounty sound`
4894	`- intelligence about Russian (score: 0.27)`
4895	`- Russian bounties (score: 0.27) → russian bounty`
4896	`- Democrat (score: 0.28) → Democrat`
4897	`- CIA (score: 0.28) → CIA`
4898	`- Department (score: 0.28) → Department`
4899	`- bounties sounded credible (score: 0.35) → bounty sound credible`
4900	`- sounded credible (score: 0.35) → sound credible`
4901	`- Russian (score: 0.36)`
4902	`Total keywords: 20 extracted in 0.0298 seconds`
4903
4904	`KeyBERT Keywords:`
4905	`- intelligence russian bounties (score: 0.60)`
4906	`- kelly representative elissa (score: 0.57)`
4907	`- kelly representative (score: 0.54)`
4908	`- elissa slotkin democrat (score: 0.53)`
4909	`- bounties sounded credible (score: 0.51)`
4910	`- russian bounties sounded (score: 0.50)`
4911	`- russian bounties (score: 0.49)`
4912	`- intelligence russian (score: 0.47)`
4913	`- representative elissa slotkin (score: 0.47)`
4914	`- asked intelligence russian (score: 0.46)`
4915	`- slotkin democrat michigan (score: 0.43)`
4916	`- democrat michigan used (score: 0.42)`
4917	`- kelly (score: 0.41)`
4918	`- slotkin democrat (score: 0.41)`
4919	`- representative elissa (score: 0.39)`
4920	`- democrat michigan (score: 0.39)`
4921	`- bounties sounded (score: 0.38)`
4922	`- work cia department (score: 0.38)`
4923	`- cia department (score: 0.37)`
4924	`- cia (score: 0.37)`
4925	`Total keywords: 20 extracted in 0.0456 seconds`
4926
4927	`Dependency Relations (extracted in 0.0118sec):`
4928
4929	`Sentence 1: KELLY: Also there was Representative Elissa Slotkin.`
4930	`KELLY (PROPN) --[nsubj]--> was (VERB)`
4931	`: (PUNCT) --[punct]--> KELLY (PROPN)`
4932	`Also (ADV) --[advmod]--> was (VERB)`
4933	`there (PRON) --[expl]--> was (VERB)`
4934	`was (VERB) --[ROOT]--> was (VERB)`
4935	`Representative (PROPN) --[compound]--> Slotkin (PROPN)`
4936	`Elissa (PROPN) --[compound]--> Slotkin (PROPN)`
4937	`Slotkin (PROPN) --[attr]--> was (VERB)`
4938	`. (PUNCT) --[punct]--> was (VERB)`
4939
4940	`Sentence 2: She's a Democrat from Michigan, used to work at both CIA and the Department of Defense.`
4941	`She (PRON) --[nsubj]--> 's (AUX)`
4942	`'s (AUX) --[ROOT]--> 's (AUX)`
4943	`a (DET) --[det]--> Democrat (PROPN)`
4944	`Democrat (PROPN) --[attr]--> 's (AUX)`
4945	`from (ADP) --[prep]--> Democrat (PROPN)`
4946	`Michigan (PROPN) --[pobj]--> from (ADP)`
4947	`, (PUNCT) --[punct]--> 's (AUX)`
4948	`used (VERB) --[advcl]--> 's (AUX)`
4949	`to (PART) --[aux]--> work (VERB)`
4950	`work (VERB) --[xcomp]--> used (VERB)`
4951	`at (ADP) --[prep]--> work (VERB)`
4952	`both (CCONJ) --[det]--> CIA (PROPN)`
4953	`CIA (PROPN) --[pobj]--> at (ADP)`
4954	`and (CCONJ) --[cc]--> CIA (PROPN)`
4955	`the (DET) --[det]--> Department (PROPN)`
4956	`Department (PROPN) --[conj]--> CIA (PROPN)`
4957	`of (ADP) --[prep]--> Department (PROPN)`
4958	`Defense (PROPN) --[pobj]--> of (ADP)`
4959	`. (PUNCT) --[punct]--> 's (AUX)`
4960
4961	`Sentence 3: I asked her whether the intelligence about Russian bounties sounded credible.`
4962	`I (PRON) --[nsubj]--> asked (VERB)`
4963	`asked (VERB) --[ROOT]--> asked (VERB)`
4964	`her (PRON) --[dobj]--> asked (VERB)`
4965	`whether (SCONJ) --[mark]--> sounded (VERB)`
4966	`the (DET) --[det]--> intelligence (NOUN)`
4967	`intelligence (NOUN) --[nsubj]--> sounded (VERB)`
4968	`about (ADP) --[prep]--> intelligence (NOUN)`
4969	`Russian (ADJ) --[amod]--> bounties (NOUN)`
4970	`bounties (NOUN) --[pobj]--> about (ADP)`
4971	`sounded (VERB) --[ccomp]--> asked (VERB)`
4972	`credible (ADJ) --[acomp]--> sounded (VERB)`
4973	`. (PUNCT) --[punct]--> asked (VERB)`
4974
4975	`Total sentences: 3`
4976
4977	`RAKE Keyphrase Relationships:`
4978	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
4979
4980	`YAKE Keyphrase Relationships:`
4981	`noun phrase: "Representative Elissa Slotkin" contains [representative elissa slotkin], [elissa slotkin], [representative elissa], [slotkin], [representative], [elissa]`
4982	`noun phrase: "Russian bounties" contains [russian bounties], [russian]`
4983	`Total relationships found: 2`
4984
4985	`Timing Comparison:`
4986	`RAKE: 0.0000sec`
4987	`YAKE: 0.0298sec`
4988	`KeyBERT: 0.0456sec`
4989	`Dependencies: 0.0118sec`
4990	`Fastest: RAKE`
4991
4992	`================================================================================`
4993	`Message 37:`
4994	`VEGA: Hi, Titi. (Speaking Spanish).`
4995	`--------------------------------------------------------------------------------`
4996	`RAKE Keywords:`
4997	`- speaking spanish ). (score: 9.00)`
4998	`- vega (score: 1.00) → VEGA`
4999	`- titi (score: 1.00) → Titi`
5000	`- hi (score: 1.00) → Hi`
5001	`Total keywords: 4 extracted in 0.0000 seconds`
5002
5003	`YAKE Keywords:`
5004	`- VEGA (score: 0.04) → VEGA`
5005	`- Titi (score: 0.04) → Titi`
5006	`- Speaking Spanish (score: 0.08) → speak Spanish`
5007	`- Speaking (score: 0.27) → speak`
5008	`- Spanish (score: 0.27) → Spanish`
5009	`Total keywords: 5 extracted in 0.0000 seconds`
5010
5011	`KeyBERT Keywords:`
5012	`- vega hi titi (score: 0.83)`
5013	`- titi speaking spanish (score: 0.75)`
5014	`- vega hi (score: 0.69)`
5015	`- hi titi speaking (score: 0.65)`
5016	`- hi titi (score: 0.63)`
5017	`- titi speaking (score: 0.60)`
5018	`- titi (score: 0.59)`
5019	`- vega (score: 0.55)`
5020	`- spanish (score: 0.50)`
5021	`- speaking spanish (score: 0.49)`
5022	`- hi (score: 0.37)`
5023	`- speaking (score: 0.22)`
5024	`Total keywords: 12 extracted in 0.0218 seconds`
5025
5026	`Dependency Relations (extracted in 0.0060sec):`
5027
5028	`Sentence 1: VEGA:`
5029	`VEGA (PROPN) --[ROOT]--> VEGA (PROPN)`
5030	`: (PUNCT) --[punct]--> VEGA (PROPN)`
5031
5032	`Sentence 2: Hi, Titi. (Speaking Spanish).`
5033	`Hi (PROPN) --[ROOT]--> Hi (PROPN)`
5034	`, (PUNCT) --[punct]--> Hi (PROPN)`
5035	`Titi (PROPN) --[npadvmod]--> Hi (PROPN)`
5036	`. (PUNCT) --[punct]--> Hi (PROPN)`
5037	`( (PUNCT) --[punct]--> Hi (PROPN)`
5038	`Speaking (VERB) --[compound]--> Spanish (PROPN)`
5039	`Spanish (PROPN) --[appos]--> Hi (PROPN)`
5040	`) (PUNCT) --[punct]--> Hi (PROPN)`
5041	`. (PUNCT) --[punct]--> Hi (PROPN)`
5042
5043	`Total sentences: 2`
5044
5045	`RAKE Keyphrase Relationships:`
5046	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
5047
5048	`YAKE Keyphrase Relationships:`
5049	`noun phrase: "Speaking Spanish" contains [speaking spanish], [speaking], [spanish]`
5050	`Total relationships found: 1`
5051
5052	`Timing Comparison:`
5053	`RAKE: 0.0000sec`
5054	`YAKE: 0.0000sec`
5055	`KeyBERT: 0.0218sec`
5056	`Dependencies: 0.0060sec`
5057	`Fastest: RAKE`
5058
5059	`================================================================================`
5060	`Message 38:`
5061	`ALLYN: But most of the policing on the platform happens by volunteer moderators who set their own norms.Bobby Allyn, NPR News, San Francisco.`
5062	`--------------------------------------------------------------------------------`
5063	`RAKE Keywords:`
5064	`- volunteer moderators (score: 4.00) → volunteer moderator`
5065	`- san francisco (score: 4.00) → San Francisco`
5066	`- platform happens (score: 4.00) → platform happen`
5067	`- npr news (score: 4.00) → NPR News`
5068	`- bobby allyn (score: 3.50) → Bobby Allyn`
5069	`- allyn (score: 1.50)`
5070	`- set (score: 1.00)`
5071	`- policing (score: 1.00)`
5072	`- norms (score: 1.00) → norm`
5073	`Total keywords: 9 extracted in 0.0000 seconds`
5074
5075	`YAKE Keywords:`
5076	`- San Francisco (score: 0.00) → San Francisco`
5077	`- norms.Bobby Allyn (score: 0.02)`
5078	`- volunteer moderators (score: 0.03) → volunteer moderator`
5079	`- moderators who set (score: 0.03) → moderator who set`
5080	`- ALLYN (score: 0.05)`
5081	`- NPR (score: 0.06) → NPR`
5082	`- San (score: 0.06) → San`
5083	`- Francisco (score: 0.06) → Francisco`
5084	`- policing (score: 0.18)`
5085	`- platform (score: 0.18)`
5086	`- volunteer (score: 0.18)`
5087	`- moderators (score: 0.18) → moderator`
5088	`- set (score: 0.18)`
5089	`- norms.Bobby (score: 0.18)`
5090	`Total keywords: 14 extracted in 0.0098 seconds`
5091
5092	`KeyBERT Keywords:`
5093	`- allyn policing platform (score: 0.69)`
5094	`- allyn policing (score: 0.69)`
5095	`- policing platform (score: 0.68)`
5096	`- policing platform happens (score: 0.68)`
5097	`- policing (score: 0.59)`
5098	`- moderators set norms (score: 0.49)`
5099	`- volunteer moderators (score: 0.47)`
5100	`- moderators (score: 0.45)`
5101	`- happens volunteer moderators (score: 0.44)`
5102	`- volunteer moderators set (score: 0.42)`
5103	`- platform happens volunteer (score: 0.41)`
5104	`- allyn npr news (score: 0.39)`
5105	`- moderators set (score: 0.37)`
5106	`- norms bobby allyn (score: 0.37)`
5107	`- norms (score: 0.36)`
5108	`- allyn npr (score: 0.35)`
5109	`- bobby allyn npr (score: 0.35)`
5110	`- npr news (score: 0.34)`
5111	`- news san francisco (score: 0.31)`
5112	`- allyn (score: 0.30)`
5113	`Total keywords: 20 extracted in 0.0361 seconds`
5114
5115	`Dependency Relations (extracted in 0.0057sec):`
5116
5117	`Sentence 1: ALLYN: But most of the policing on the platform happens by volunteer moderators who set their own norms.`
5118	`ALLYN (NOUN) --[ROOT]--> ALLYN (NOUN)`
5119	`: (PUNCT) --[punct]--> ALLYN (NOUN)`
5120	`But (CCONJ) --[cc]--> happens (VERB)`
5121	`most (ADJ) --[nsubj]--> happens (VERB)`
5122	`of (ADP) --[prep]--> most (ADJ)`
5123	`the (DET) --[det]--> policing (NOUN)`
5124	`policing (NOUN) --[pobj]--> of (ADP)`
5125	`on (ADP) --[prep]--> policing (NOUN)`
5126	`the (DET) --[det]--> platform (NOUN)`
5127	`platform (NOUN) --[pobj]--> on (ADP)`
5128	`happens (VERB) --[conj]--> ALLYN (NOUN)`
5129	`by (ADP) --[prep]--> happens (VERB)`
5130	`volunteer (NOUN) --[compound]--> moderators (NOUN)`
5131	`moderators (NOUN) --[pobj]--> by (ADP)`
5132	`who (PRON) --[nsubj]--> set (VERB)`
5133	`set (VERB) --[relcl]--> moderators (NOUN)`
5134	`their (PRON) --[poss]--> norms (NOUN)`
5135	`own (ADJ) --[amod]--> norms (NOUN)`
5136	`norms (NOUN) --[dobj]--> set (VERB)`
5137	`. (PUNCT) --[punct]--> happens (VERB)`
5138
5139	`Sentence 2: Bobby Allyn, NPR News, San Francisco.`
5140	`Bobby (PROPN) --[compound]--> Allyn (PROPN)`
5141	`Allyn (PROPN) --[ROOT]--> Allyn (PROPN)`
5142	`, (PUNCT) --[punct]--> Allyn (PROPN)`
5143	`NPR (PROPN) --[compound]--> News (PROPN)`
5144	`News (PROPN) --[appos]--> Allyn (PROPN)`
5145	`, (PUNCT) --[punct]--> News (PROPN)`
5146	`San (PROPN) --[compound]--> Francisco (PROPN)`
5147	`Francisco (PROPN) --[npadvmod]--> News (PROPN)`
5148	`. (PUNCT) --[punct]--> Allyn (PROPN)`
5149
5150	`Total sentences: 2`
5151
5152	`RAKE Keyphrase Relationships:`
5153	`noun phrase: "Bobby Allyn" contains [bobby allyn], [allyn]`
5154	`verb phrase: "set norms" contains [set], [norms]`
5155	`Total relationships found: 2`
5156
5157	`YAKE Keyphrase Relationships:`
5158	`noun phrase: "volunteer moderators" contains [volunteer moderators], [volunteer], [moderators]`
5159	`Total relationships found: 1`
5160
5161	`Timing Comparison:`
5162	`RAKE: 0.0000sec`
5163	`YAKE: 0.0098sec`
5164	`KeyBERT: 0.0361sec`
5165	`Dependencies: 0.0057sec`
5166	`Fastest: RAKE`
5167
5168	`================================================================================`
5169	`Message 39:`
5170	`BOWMAN: You're welcome.`
5171	`--------------------------------------------------------------------------------`
5172	`RAKE Keywords:`
5173	`- welcome (score: 1.00)`
5174	`- bowman (score: 1.00) → BOWMAN`
5175	`Total keywords: 2 extracted in 0.0000 seconds`
5176
5177	`YAKE Keywords:`
5178	`- BOWMAN (score: 0.03) → BOWMAN`
5179	`Total keywords: 1 extracted in 0.0000 seconds`
5180
5181	`KeyBERT Keywords:`
5182	`- bowman welcome (score: 0.82)`
5183	`- bowman (score: 0.64)`
5184	`- welcome (score: 0.39)`
5185	`Total keywords: 3 extracted in 0.0138 seconds`
5186
5187	`Dependency Relations (extracted in 0.0060sec):`
5188
5189	`Sentence 1: BOWMAN: You're welcome.`
5190	`BOWMAN (PROPN) --[npadvmod]--> 're (AUX)`
5191	`: (PUNCT) --[punct]--> 're (AUX)`
5192	`You (PRON) --[nsubj]--> 're (AUX)`
5193	`'re (AUX) --[ROOT]--> 're (AUX)`
5194	`welcome (ADJ) --[acomp]--> 're (AUX)`
5195	`. (PUNCT) --[punct]--> 're (AUX)`
5196
5197	`Total sentences: 1`
5198
5199	`RAKE Keyphrase Relationships:`
5200	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
5201
5202	`YAKE Keyphrase Relationships:`
5203	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
5204
5205	`Timing Comparison:`
5206	`RAKE: 0.0000sec`
5207	`YAKE: 0.0000sec`
5208	`KeyBERT: 0.0138sec`
5209	`Dependencies: 0.0060sec`
5210	`Fastest: RAKE`
5211
5212	`================================================================================`
5213	`Message 40:`
5214	`CAMPBELL: Yeah, it's been pretty swift and angry. Lawmakers here - you know, Democrats, Republicans were pretty quick to condemn what he did. Lauren Boebert, our newly elected Republican congresswoman, said, and I'll read you a quote, "when Denver Mayor Michael Hancock goes against his own orders, Denver residents need to stop taking orders from him." You know, a restaurant in Denver wrote on its marquee, enjoy your trip, Mayor Hancock. So yeah, it's been pretty intense.`
5215	`--------------------------------------------------------------------------------`
5216	`RAKE Keywords:`
5217	`- denver residents need (score: 8.50) → Denver resident need`
5218	`- stop taking orders (score: 8.00) → stop take order`
5219	`- denver wrote (score: 4.50) → Denver write`
5220	`- pretty swift (score: 4.00)`
5221	`- pretty quick (score: 4.00)`
5222	`- pretty intense (score: 4.00)`
5223	`- mayor hancock (score: 4.00) → Mayor Hancock`
5224	`- lauren boebert (score: 4.00) → Lauren Boebert`
5225	`- orders (score: 2.00) → order`
5226	`- yeah (score: 1.00)`
5227	`- yeah (score: 1.00)`
5228	`- trip (score: 1.00)`
5229	`- said (score: 1.00) → say`
5230	`- restaurant (score: 1.00)`
5231	`- republicans (score: 1.00) → Republicans`
5232	`- read (score: 1.00)`
5233	`- quote (score: 1.00)`
5234	`- marquee (score: 1.00)`
5235	`- lawmakers (score: 1.00) → lawmaker`
5236	`- know (score: 1.00)`
5237	`- know (score: 1.00)`
5238	`- enjoy (score: 1.00)`
5239	`- democrats (score: 1.00) → Democrats`
5240	`- condemn (score: 1.00)`
5241	`- campbell (score: 1.00) → CAMPBELL`
5242	`- angry (score: 1.00)`
5243	`- ." (score: 1.00)`
5244	`Total keywords: 27 extracted in 0.0000 seconds`
5245
5246	`YAKE Keywords:`
5247	`- swift and angry (score: 0.05)`
5248	`- CAMPBELL (score: 0.05) → CAMPBELL`
5249	`- Mayor Michael Hancock (score: 0.09) → Mayor Michael Hancock`
5250	`- Denver Mayor Michael (score: 0.10) → Denver Mayor Michael`
5251	`- Denver (score: 0.14) → Denver`
5252	`- pretty swift (score: 0.16)`
5253	`- Democrats (score: 0.16) → Democrats`
5254	`- Mayor Michael (score: 0.17) → Mayor Michael`
5255	`- Michael Hancock (score: 0.17) → Michael Hancock`
5256	`- pretty (score: 0.17)`
5257	`- Yeah (score: 0.18)`
5258	`- Lauren Boebert (score: 0.18) → Lauren Boebert`
5259	`- angry (score: 0.19)`
5260	`- Mayor Hancock (score: 0.20) → Mayor Hancock`
5261	`- Mayor (score: 0.20) → Mayor`
5262	`- Hancock (score: 0.20) → Hancock`
5263	`- Denver Mayor (score: 0.21) → Denver Mayor`
5264	`- Republican congresswoman (score: 0.23)`
5265	`- elected Republican congresswoman (score: 0.24) → elect republican congresswoman`
5266	`- swift (score: 0.26)`
5267	`Total keywords: 20 extracted in 0.0241 seconds`
5268
5269	`KeyBERT Keywords:`
5270	`- quote denver mayor (score: 0.55)`
5271	`- denver mayor (score: 0.50)`
5272	`- denver mayor michael (score: 0.48)`
5273	`- trip mayor hancock (score: 0.47)`
5274	`- goes orders denver (score: 0.45)`
5275	`- enjoy trip mayor (score: 0.44)`
5276	`- mayor michael hancock (score: 0.44)`
5277	`- mayor hancock yeah (score: 0.43)`
5278	`- mayor hancock (score: 0.42)`
5279	`- orders denver residents (score: 0.41)`
5280	`- read quote denver (score: 0.41)`
5281	`- orders denver (score: 0.40)`
5282	`- quote denver (score: 0.38)`
5283	`- trip mayor (score: 0.38)`
5284	`- denver wrote (score: 0.37)`
5285	`- swift angry lawmakers (score: 0.37)`
5286	`- denver wrote marquee (score: 0.37)`
5287	`- restaurant denver (score: 0.37)`
5288	`- restaurant denver wrote (score: 0.37)`
5289	`- pretty swift angry (score: 0.37)`
5290	`Total keywords: 20 extracted in 0.0770 seconds`
5291
5292	`Dependency Relations (extracted in 0.0098sec):`
5293
5294	`Sentence 1: CAMPBELL:`
5295	`CAMPBELL (PROPN) --[ROOT]--> CAMPBELL (PROPN)`
5296	`: (PUNCT) --[punct]--> CAMPBELL (PROPN)`
5297
5298	`Sentence 2: Yeah, it's been pretty swift and angry.`
5299	`Yeah (INTJ) --[intj]--> been (AUX)`
5300	`, (PUNCT) --[punct]--> been (AUX)`
5301	`it (PRON) --[nsubjpass]--> been (AUX)`
5302	`'s (AUX) --[auxpass]--> been (AUX)`
5303	`been (AUX) --[ROOT]--> been (AUX)`
5304	`pretty (ADV) --[advmod]--> swift (ADJ)`
5305	`swift (ADJ) --[acomp]--> been (AUX)`
5306	`and (CCONJ) --[cc]--> swift (ADJ)`
5307	`angry (ADJ) --[conj]--> swift (ADJ)`
5308	`. (PUNCT) --[punct]--> been (AUX)`
5309
5310	`Sentence 3: Lawmakers here - you know, Democrats, Republicans were pretty quick to condemn what he did.`
5311	`Lawmakers (NOUN) --[nsubj]--> were (AUX)`
5312	`here (ADV) --[advmod]--> Lawmakers (NOUN)`
5313	`- (PUNCT) --[punct]--> were (AUX)`
5314	`you (PRON) --[nsubj]--> know (VERB)`
5315	`know (VERB) --[parataxis]--> were (AUX)`
5316	`, (PUNCT) --[punct]--> were (AUX)`
5317	`Democrats (PROPN) --[prep]--> , (PUNCT)`
5318	`, (PUNCT) --[punct]--> were (AUX)`
5319	`Republicans (PROPN) --[nsubj]--> were (AUX)`
5320	`were (AUX) --[ROOT]--> were (AUX)`
5321	`pretty (ADV) --[advmod]--> quick (ADJ)`
5322	`quick (ADJ) --[acomp]--> were (AUX)`
5323	`to (PART) --[aux]--> condemn (VERB)`
5324	`condemn (VERB) --[xcomp]--> quick (ADJ)`
5325	`what (PRON) --[dobj]--> did (VERB)`
5326	`he (PRON) --[nsubj]--> did (VERB)`
5327	`did (VERB) --[ccomp]--> condemn (VERB)`
5328	`. (PUNCT) --[punct]--> were (AUX)`
5329
5330	`Sentence 4: Lauren Boebert, our newly elected Republican congresswoman, said, and I'll read you a quote, "when Denver Mayor Michael Hancock goes against his own orders, Denver residents need to stop taking orders from him."`
5331	`Lauren (PROPN) --[compound]--> Boebert (PROPN)`
5332	`Boebert (PROPN) --[nsubj]--> said (VERB)`
5333	`, (PUNCT) --[punct]--> Boebert (PROPN)`
5334	`our (PRON) --[poss]--> congresswoman (NOUN)`
5335	`newly (ADV) --[advmod]--> elected (VERB)`
5336	`elected (VERB) --[amod]--> congresswoman (NOUN)`
5337	`Republican (ADJ) --[amod]--> congresswoman (NOUN)`
5338	`congresswoman (NOUN) --[appos]--> Boebert (PROPN)`
5339	`, (PUNCT) --[punct]--> Boebert (PROPN)`
5340	`said (VERB) --[ROOT]--> said (VERB)`
5341	`, (PUNCT) --[punct]--> said (VERB)`
5342	`and (CCONJ) --[cc]--> said (VERB)`
5343	`I (PRON) --[nsubj]--> read (VERB)`
5344	`'ll (AUX) --[aux]--> read (VERB)`
5345	`read (VERB) --[conj]--> said (VERB)`
5346	`you (PRON) --[dative]--> read (VERB)`
5347	`a (DET) --[det]--> quote (NOUN)`
5348	`quote (NOUN) --[dobj]--> read (VERB)`
5349	`, (PUNCT) --[punct]--> read (VERB)`
5350	`" (PUNCT) --[punct]--> read (VERB)`
5351	`when (SCONJ) --[advmod]--> goes (VERB)`
5352	`Denver (PROPN) --[compound]--> Mayor (PROPN)`
5353	`Mayor (PROPN) --[compound]--> Hancock (PROPN)`
5354	`Michael (PROPN) --[compound]--> Hancock (PROPN)`
5355	`Hancock (PROPN) --[nsubj]--> goes (VERB)`
5356	`goes (VERB) --[advcl]--> need (VERB)`
5357	`against (ADP) --[prep]--> goes (VERB)`
5358	`his (PRON) --[poss]--> orders (NOUN)`
5359	`own (ADJ) --[amod]--> orders (NOUN)`
5360	`orders (NOUN) --[pobj]--> against (ADP)`
5361	`, (PUNCT) --[punct]--> need (VERB)`
5362	`Denver (PROPN) --[compound]--> residents (NOUN)`
5363	`residents (NOUN) --[nsubj]--> need (VERB)`
5364	`need (VERB) --[ccomp]--> read (VERB)`
5365	`to (PART) --[aux]--> stop (VERB)`
5366	`stop (VERB) --[xcomp]--> need (VERB)`
5367	`taking (VERB) --[xcomp]--> stop (VERB)`
5368	`orders (NOUN) --[dobj]--> taking (VERB)`
5369	`from (ADP) --[prep]--> taking (VERB)`
5370	`him (PRON) --[pobj]--> from (ADP)`
5371	`. (PUNCT) --[punct]--> read (VERB)`
5372	`" (PUNCT) --[punct]--> said (VERB)`
5373
5374	`Sentence 5: You know, a restaurant in Denver wrote on its marquee, enjoy your trip, Mayor Hancock.`
5375	`You (PRON) --[nsubj]--> know (VERB)`
5376	`know (VERB) --[parataxis]--> wrote (VERB)`
5377	`, (PUNCT) --[punct]--> wrote (VERB)`
5378	`a (DET) --[det]--> restaurant (NOUN)`
5379	`restaurant (NOUN) --[nsubj]--> wrote (VERB)`
5380	`in (ADP) --[prep]--> restaurant (NOUN)`
5381	`Denver (PROPN) --[pobj]--> in (ADP)`
5382	`wrote (VERB) --[ROOT]--> wrote (VERB)`
5383	`on (ADP) --[prep]--> wrote (VERB)`
5384	`its (PRON) --[poss]--> marquee (NOUN)`
5385	`marquee (NOUN) --[pobj]--> on (ADP)`
5386	`, (PUNCT) --[punct]--> wrote (VERB)`
5387	`enjoy (VERB) --[conj]--> wrote (VERB)`
5388	`your (PRON) --[poss]--> trip (NOUN)`
5389	`trip (NOUN) --[dobj]--> enjoy (VERB)`
5390	`, (PUNCT) --[punct]--> trip (NOUN)`
5391	`Mayor (PROPN) --[compound]--> Hancock (PROPN)`
5392	`Hancock (PROPN) --[appos]--> trip (NOUN)`
5393	`. (PUNCT) --[punct]--> wrote (VERB)`
5394
5395	`Sentence 6: So yeah, it's been pretty intense.`
5396	`So (ADV) --[advmod]--> been (AUX)`
5397	`yeah (INTJ) --[intj]--> been (AUX)`
5398	`, (PUNCT) --[punct]--> been (AUX)`
5399	`it (PRON) --[nsubjpass]--> been (AUX)`
5400	`'s (AUX) --[auxpass]--> been (AUX)`
5401	`been (AUX) --[ROOT]--> been (AUX)`
5402	`pretty (ADV) --[advmod]--> intense (ADJ)`
5403	`intense (ADJ) --[acomp]--> been (AUX)`
5404	`. (PUNCT) --[punct]--> been (AUX)`
5405
5406	`Total sentences: 6`
5407
5408	`RAKE Keyphrase Relationships:`
5409	`verb phrase: "read 'll quote" contains [read], [quote]`
5410	`verb phrase: "enjoy trip" contains [trip], [enjoy]`
5411	`Total relationships found: 2`
5412
5413	`YAKE Keyphrase Relationships:`
5414	`noun phrase: "our newly elected Republican congresswoman" contains [republican congresswoman], [elected republican congresswoman]`
5415	`noun phrase: "Denver Mayor Michael Hancock" contains [mayor michael hancock], [denver mayor michael], [denver], [mayor michael], [michael hancock], [mayor], [hancock], [denver mayor]`
5416	`noun phrase: "Mayor Hancock" contains [mayor hancock], [mayor], [hancock]`
5417	`Total relationships found: 3`
5418
5419	`Timing Comparison:`
5420	`RAKE: 0.0000sec`
5421	`YAKE: 0.0241sec`
5422	`KeyBERT: 0.0770sec`
5423	`Dependencies: 0.0098sec`
5424	`Fastest: RAKE`
5425
5426	`================================================================================`
5427	`Message 41:`
5428	`CARMEN PONCE: (Speaking Spanish).`
5429	`--------------------------------------------------------------------------------`
5430	`RAKE Keywords:`
5431	`- speaking spanish ). (score: 9.00)`
5432	`- carmen ponce (score: 4.00) → CARMEN PONCE`
5433	`Total keywords: 2 extracted in 0.0000 seconds`
5434
5435	`YAKE Keywords:`
5436	`- CARMEN PONCE (score: 0.01) → CARMEN PONCE`
5437	`- Speaking Spanish (score: 0.01) → speak Spanish`
5438	`- CARMEN (score: 0.09) → CARMEN`
5439	`- PONCE (score: 0.09) → PONCE`
5440	`- Speaking (score: 0.09) → speak`
5441	`- Spanish (score: 0.09) → Spanish`
5442	`Total keywords: 6 extracted in 0.0017 seconds`
5443
5444	`KeyBERT Keywords:`
5445	`- carmen ponce (score: 0.88)`
5446	`- carmen ponce speaking (score: 0.88)`
5447	`- ponce speaking spanish (score: 0.77)`
5448	`- ponce speaking (score: 0.70)`
5449	`- ponce (score: 0.69)`
5450	`- carmen (score: 0.66)`
5451	`- spanish (score: 0.55)`
5452	`- speaking spanish (score: 0.55)`
5453	`- speaking (score: 0.17)`
5454	`Total keywords: 9 extracted in 0.0167 seconds`
5455
5456	`Dependency Relations (extracted in 0.0040sec):`
5457
5458	`Sentence 1: CARMEN PONCE: (Speaking Spanish).`
5459	`CARMEN (PROPN) --[compound]--> PONCE (PROPN)`
5460	`PONCE (PROPN) --[ROOT]--> PONCE (PROPN)`
5461	`: (PUNCT) --[punct]--> PONCE (PROPN)`
5462	`( (PUNCT) --[punct]--> PONCE (PROPN)`
5463	`Speaking (VERB) --[acl]--> PONCE (PROPN)`
5464	`Spanish (PROPN) --[dobj]--> Speaking (VERB)`
5465	`) (PUNCT) --[punct]--> PONCE (PROPN)`
5466	`. (PUNCT) --[punct]--> PONCE (PROPN)`
5467
5468	`Total sentences: 1`
5469
5470	`RAKE Keyphrase Relationships:`
5471	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
5472
5473	`YAKE Keyphrase Relationships:`
5474	`noun phrase: "CARMEN PONCE" contains [carmen ponce], [carmen], [ponce]`
5475	`verb phrase: "Speaking Spanish" contains [speaking spanish], [speaking], [spanish]`
5476	`Total relationships found: 2`
5477
5478	`Timing Comparison:`
5479	`RAKE: 0.0000sec`
5480	`YAKE: 0.0017sec`
5481	`KeyBERT: 0.0167sec`
5482	`Dependencies: 0.0040sec`
5483	`Fastest: RAKE`
5484
5485	`================================================================================`
5486	`Message 42:`
5487	`ANAS BABA: Yeah. Hi. Sorry. The signal isn't that, like, perfect.`
5488	`--------------------------------------------------------------------------------`
5489	`RAKE Keywords:`
5490	`- anas baba (score: 4.00) → ANAS BABA`
5491	`- yeah (score: 1.00)`
5492	`- sorry (score: 1.00)`
5493	`- signal (score: 1.00)`
5494	`- perfect (score: 1.00)`
5495	`- like (score: 1.00)`
5496	`- hi (score: 1.00)`
5497	`Total keywords: 7 extracted in 0.0000 seconds`
5498
5499	`YAKE Keywords:`
5500	`- ANAS BABA (score: 0.01) → ANAS BABA`
5501	`- Yeah (score: 0.04)`
5502	`- ANAS (score: 0.10) → ANAS`
5503	`- BABA (score: 0.10) → BABA`
5504	`- perfect (score: 0.32)`
5505	`- signal (score: 0.81)`
5506	`Total keywords: 6 extracted in 0.0010 seconds`
5507
5508	`KeyBERT Keywords:`
5509	`- sorry signal isn (score: 0.56)`
5510	`- anas baba yeah (score: 0.54)`
5511	`- signal isn like (score: 0.54)`
5512	`- hi sorry signal (score: 0.53)`
5513	`- baba yeah hi (score: 0.52)`
5514	`- anas baba (score: 0.52)`
5515	`- signal isn (score: 0.52)`
5516	`- signal (score: 0.49)`
5517	`- sorry signal (score: 0.48)`
5518	`- baba yeah (score: 0.43)`
5519	`- baba (score: 0.40)`
5520	`- yeah hi sorry (score: 0.34)`
5521	`- anas (score: 0.31)`
5522	`- yeah hi (score: 0.30)`
5523	`- hi sorry (score: 0.28)`
5524	`- hi (score: 0.28)`
5525	`- isn like perfect (score: 0.23)`
5526	`- perfect (score: 0.21)`
5527	`- sorry (score: 0.20)`
5528	`- like perfect (score: 0.18)`
5529	`Total keywords: 20 extracted in 0.0217 seconds`
5530
5531	`Dependency Relations (extracted in 0.0060sec):`
5532
5533	`Sentence 1: ANAS BABA:`
5534	`ANAS (PROPN) --[compound]--> BABA (PROPN)`
5535	`BABA (PROPN) --[ROOT]--> BABA (PROPN)`
5536	`: (PUNCT) --[punct]--> BABA (PROPN)`
5537
5538	`Sentence 2: Yeah.`
5539	`Yeah (INTJ) --[ROOT]--> Yeah (INTJ)`
5540	`. (PUNCT) --[punct]--> Yeah (INTJ)`
5541
5542	`Sentence 3: Hi.`
5543	`Hi (INTJ) --[ROOT]--> Hi (INTJ)`
5544	`. (PUNCT) --[punct]--> Hi (INTJ)`
5545
5546	`Sentence 4: Sorry.`
5547	`Sorry (INTJ) --[ROOT]--> Sorry (INTJ)`
5548	`. (PUNCT) --[punct]--> Sorry (INTJ)`
5549
5550	`Sentence 5: The signal isn't that, like, perfect.`
5551	`The (DET) --[det]--> signal (NOUN)`
5552	`signal (NOUN) --[nsubj]--> is (AUX)`
5553	`is (AUX) --[ROOT]--> is (AUX)`
5554	`n't (PART) --[neg]--> is (AUX)`
5555	`that (PRON) --[advmod]--> perfect (ADJ)`
5556	`, (PUNCT) --[punct]--> perfect (ADJ)`
5557	`like (INTJ) --[intj]--> perfect (ADJ)`
5558	`, (PUNCT) --[punct]--> perfect (ADJ)`
5559	`perfect (ADJ) --[acomp]--> is (AUX)`
5560	`. (PUNCT) --[punct]--> is (AUX)`
5561
5562	`Total sentences: 5`
5563
5564	`RAKE Keyphrase Relationships:`
5565	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
5566
5567	`YAKE Keyphrase Relationships:`
5568	`noun phrase: "ANAS BABA" contains [anas baba], [anas], [baba]`
5569	`Total relationships found: 1`
5570
5571	`Timing Comparison:`
5572	`RAKE: 0.0000sec`
5573	`YAKE: 0.0010sec`
5574	`KeyBERT: 0.0217sec`
5575	`Dependencies: 0.0060sec`
5576	`Fastest: RAKE`
5577
5578	`================================================================================`
5579	`Message 43:`
5580	`CHANG: So can you just describe - what does this camp look like right now?`
5581	`--------------------------------------------------------------------------------`
5582	`RAKE Keywords:`
5583	`- describe (score: 1.00)`
5584	`- chang (score: 1.00) → CHANG`
5585	`Total keywords: 2 extracted in 0.0000 seconds`
5586
5587	`YAKE Keywords:`
5588	`- CHANG (score: 0.03) → CHANG`
5589	`- describe (score: 0.16)`
5590	`- camp (score: 0.30)`
5591	`Total keywords: 3 extracted in 0.0020 seconds`
5592
5593	`KeyBERT Keywords:`
5594	`- camp look like (score: 0.68)`
5595	`- does camp look (score: 0.64)`
5596	`- camp look (score: 0.58)`
5597	`- camp (score: 0.56)`
5598	`- does camp (score: 0.54)`
5599	`- just does camp (score: 0.46)`
5600	`- chang (score: 0.46)`
5601	`- chang just (score: 0.41)`
5602	`- chang just does (score: 0.38)`
5603	`- look like (score: 0.31)`
5604	`- look like right (score: 0.24)`
5605	`- like (score: 0.16)`
5606	`- look (score: 0.15)`
5607	`- like right (score: 0.15)`
5608	`- just (score: 0.10)`
5609	`- just does (score: 0.08)`
5610	`- does (score: 0.04)`
5611	`- right (score: 0.04)`
5612	`Total keywords: 18 extracted in 0.0200 seconds`
5613
5614	`Dependency Relations (extracted in 0.0060sec):`
5615
5616	`Sentence 1: CHANG: So can you just describe - what does this camp look like right now?`
5617	`CHANG (PROPN) --[ROOT]--> CHANG (PROPN)`
5618	`: (PUNCT) --[punct]--> CHANG (PROPN)`
5619	`So (ADV) --[advmod]--> describe (VERB)`
5620	`can (AUX) --[aux]--> describe (VERB)`
5621	`you (PRON) --[nsubj]--> describe (VERB)`
5622	`just (ADV) --[advmod]--> describe (VERB)`
5623	`describe (VERB) --[acl]--> CHANG (PROPN)`
5624	`- (PUNCT) --[punct]--> describe (VERB)`
5625	`what (PRON) --[pobj]--> look (VERB)`
5626	`does (AUX) --[aux]--> look (VERB)`
5627	`this (DET) --[det]--> camp (NOUN)`
5628	`camp (NOUN) --[nsubj]--> look (VERB)`
5629	`look (VERB) --[punct]--> CHANG (PROPN)`
5630	`like (INTJ) --[intj]--> look (VERB)`
5631	`right (ADV) --[advmod]--> now (ADV)`
5632	`now (ADV) --[advmod]--> look (VERB)`
5633	`? (PUNCT) --[punct]--> look (VERB)`
5634
5635	`Total sentences: 1`
5636
5637	`RAKE Keyphrase Relationships:`
5638	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
5639
5640	`YAKE Keyphrase Relationships:`
5641	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
5642
5643	`Timing Comparison:`
5644	`RAKE: 0.0000sec`
5645	`YAKE: 0.0020sec`
5646	`KeyBERT: 0.0200sec`
5647	`Dependencies: 0.0060sec`
5648	`Fastest: RAKE`
5649
5650	`================================================================================`
5651	`Message 44:`
5652	`MYRE: And he says the same is true with nuclear weapons. Again, they can inflict the huge casualties, terrorize a population, but it's not clear they'd produce the desired military outcome.`
5653	`--------------------------------------------------------------------------------`
5654	`RAKE Keywords:`
5655	`- desired military outcome (score: 9.00) → desire military outcome`
5656	`- nuclear weapons (score: 4.00) → nuclear weapon`
5657	`- huge casualties (score: 4.00) → huge casualty`
5658	`- true (score: 1.00)`
5659	`- terrorize (score: 1.00)`
5660	`- says (score: 1.00) → say`
5661	`- produce (score: 1.00)`
5662	`- population (score: 1.00)`
5663	`- myre (score: 1.00) → MYRE`
5664	`- inflict (score: 1.00)`
5665	`- clear (score: 1.00)`
5666	`Total keywords: 11 extracted in 0.0000 seconds`
5667
5668	`YAKE Keywords:`
5669	`- nuclear weapons (score: 0.02) → nuclear weapon`
5670	`- true with nuclear (score: 0.02)`
5671	`- MYRE (score: 0.04) → MYRE`
5672	`- desired military outcome (score: 0.05) → desire military outcome`
5673	`- terrorize a population (score: 0.08)`
5674	`- weapons (score: 0.10) → weapon`
5675	`- huge casualties (score: 0.12) → huge casualty`
5676	`- military outcome (score: 0.12)`
5677	`- true (score: 0.15)`
5678	`- nuclear (score: 0.15)`
5679	`- inflict the huge (score: 0.17)`
5680	`- clear they produce (score: 0.17)`
5681	`- produce the desired (score: 0.17) → produce the desire`
5682	`- desired military (score: 0.17) → desire military`
5683	`- casualties (score: 0.28) → casualty`
5684	`- terrorize (score: 0.28)`
5685	`- population (score: 0.28)`
5686	`- outcome (score: 0.28)`
5687	`- inflict (score: 0.38)`
5688	`- huge (score: 0.38)`
5689	`Total keywords: 20 extracted in 0.0213 seconds`
5690
5691	`KeyBERT Keywords:`
5692	`- true nuclear weapons (score: 0.57)`
5693	`- nuclear weapons inflict (score: 0.53)`
5694	`- says true nuclear (score: 0.50)`
5695	`- casualties terrorize population (score: 0.50)`
5696	`- nuclear weapons (score: 0.47)`
5697	`- huge casualties terrorize (score: 0.46)`
5698	`- casualties terrorize (score: 0.45)`
5699	`- terrorize population (score: 0.43)`
5700	`- true nuclear (score: 0.43)`
5701	`- terrorize population clear (score: 0.40)`
5702	`- produce desired military (score: 0.38)`
5703	`- inflict huge casualties (score: 0.35)`
5704	`- nuclear (score: 0.34)`
5705	`- military outcome (score: 0.34)`
5706	`- desired military outcome (score: 0.32)`
5707	`- weapons inflict huge (score: 0.32)`
5708	`- weapons inflict (score: 0.32)`
5709	`- myre says true (score: 0.30)`
5710	`- huge casualties (score: 0.29)`
5711	`- terrorize (score: 0.28)`
5712	`Total keywords: 20 extracted in 0.0335 seconds`
5713
5714	`Dependency Relations (extracted in 0.0000sec):`
5715
5716	`Sentence 1: MYRE:`
5717	`MYRE (PROPN) --[ROOT]--> MYRE (PROPN)`
5718	`: (PUNCT) --[punct]--> MYRE (PROPN)`
5719
5720	`Sentence 2: And he says the same is true with nuclear weapons.`
5721	`And (CCONJ) --[cc]--> says (VERB)`
5722	`he (PRON) --[nsubj]--> says (VERB)`
5723	`says (VERB) --[ROOT]--> says (VERB)`
5724	`the (DET) --[det]--> same (ADJ)`
5725	`same (ADJ) --[nsubj]--> is (AUX)`
5726	`is (AUX) --[ccomp]--> says (VERB)`
5727	`true (ADJ) --[acomp]--> is (AUX)`
5728	`with (ADP) --[prep]--> true (ADJ)`
5729	`nuclear (ADJ) --[amod]--> weapons (NOUN)`
5730	`weapons (NOUN) --[pobj]--> with (ADP)`
5731	`. (PUNCT) --[punct]--> says (VERB)`
5732
5733	`Sentence 3: Again, they can inflict the huge casualties, terrorize a population, but it's not clear they'd produce the desired military outcome.`
5734	`Again (ADV) --[advmod]--> inflict (VERB)`
5735	`, (PUNCT) --[punct]--> inflict (VERB)`
5736	`they (PRON) --[nsubj]--> inflict (VERB)`
5737	`can (AUX) --[aux]--> inflict (VERB)`
5738	`inflict (VERB) --[ROOT]--> inflict (VERB)`
5739	`the (DET) --[det]--> casualties (NOUN)`
5740	`huge (ADJ) --[amod]--> casualties (NOUN)`
5741	`casualties (NOUN) --[dobj]--> inflict (VERB)`
5742	`, (PUNCT) --[punct]--> inflict (VERB)`
5743	`terrorize (VERB) --[conj]--> inflict (VERB)`
5744	`a (DET) --[det]--> population (NOUN)`
5745	`population (NOUN) --[dobj]--> terrorize (VERB)`
5746	`, (PUNCT) --[punct]--> inflict (VERB)`
5747	`but (CCONJ) --[cc]--> inflict (VERB)`
5748	`it (PRON) --[nsubj]--> 's (AUX)`
5749	`'s (AUX) --[conj]--> inflict (VERB)`
5750	`not (PART) --[neg]--> 's (AUX)`
5751	`clear (ADJ) --[acomp]--> 's (AUX)`
5752	`they (PRON) --[nsubj]--> produce (VERB)`
5753	`'d (AUX) --[aux]--> produce (VERB)`
5754	`produce (VERB) --[ccomp]--> 's (AUX)`
5755	`the (DET) --[det]--> outcome (NOUN)`
5756	`desired (VERB) --[amod]--> outcome (NOUN)`
5757	`military (ADJ) --[amod]--> outcome (NOUN)`
5758	`outcome (NOUN) --[dobj]--> produce (VERB)`
5759	`. (PUNCT) --[punct]--> 's (AUX)`
5760
5761	`Total sentences: 3`
5762
5763	`RAKE Keyphrase Relationships:`
5764	`noun phrase: "nuclear weapons" contains [nuclear weapons], [clear]`
5765	`verb phrase: "terrorize population" contains [terrorize], [population]`
5766	`Total relationships found: 2`
5767
5768	`YAKE Keyphrase Relationships:`
5769	`noun phrase: "nuclear weapons" contains [nuclear weapons], [weapons], [nuclear]`
5770	`noun phrase: "the huge casualties" contains [huge casualties], [casualties], [huge]`
5771	`noun phrase: "the desired military outcome" contains [desired military outcome], [military outcome], [desired military], [outcome]`
5772	`verb phrase: "inflict Again can casualties" contains [casualties], [inflict]`
5773	`verb phrase: "terrorize population" contains [terrorize], [population]`
5774	`Total relationships found: 5`
5775
5776	`Timing Comparison:`
5777	`RAKE: 0.0000sec`
5778	`YAKE: 0.0213sec`
5779	`KeyBERT: 0.0335sec`
5780	`Dependencies: 0.0000sec`
5781	`Fastest: RAKE`
5782
5783	`================================================================================`
5784	`Message 45:`
5785	`NEWSOM: I have deep respect, reverence for Joe Biden as a person, his character, his decency and his capacity to do great things. That's why I'm not worthy of that conversation.`
5786	`--------------------------------------------------------------------------------`
5787	`RAKE Keywords:`
5788	`- joe biden (score: 4.00) → Joe Biden`
5789	`- great things (score: 4.00) → great thing`
5790	`- deep respect (score: 4.00)`
5791	`- worthy (score: 1.00)`
5792	`- reverence (score: 1.00)`
5793	`- person (score: 1.00)`
5794	`- newsom (score: 1.00)`
5795	`- decency (score: 1.00)`
5796	`- conversation (score: 1.00)`
5797	`- character (score: 1.00)`
5798	`- capacity (score: 1.00)`
5799	`Total keywords: 11 extracted in 0.0000 seconds`
5800
5801	`YAKE Keywords:`
5802	`- Joe Biden (score: 0.01) → Joe Biden`
5803	`- reverence for Joe (score: 0.01) → reverence for Joe`
5804	`- deep respect (score: 0.02)`
5805	`- great things (score: 0.02) → great thing`
5806	`- NEWSOM (score: 0.04)`
5807	`- Joe (score: 0.08) → Joe`
5808	`- Biden (score: 0.08) → Biden`
5809	`- respect (score: 0.10)`
5810	`- reverence (score: 0.10)`
5811	`- person (score: 0.10)`
5812	`- character (score: 0.10)`
5813	`- things (score: 0.10) → thing`
5814	`- deep (score: 0.15)`
5815	`- decency (score: 0.15)`
5816	`- capacity (score: 0.15)`
5817	`- great (score: 0.15)`
5818	`- conversation (score: 0.28)`
5819	`- worthy (score: 0.38)`
5820	`Total keywords: 18 extracted in 0.0180 seconds`
5821
5822	`KeyBERT Keywords:`
5823	`- reverence joe biden (score: 0.75)`
5824	`- joe biden (score: 0.66)`
5825	`- newsom deep respect (score: 0.65)`
5826	`- joe biden person (score: 0.63)`
5827	`- biden person character (score: 0.57)`
5828	`- newsom deep (score: 0.55)`
5829	`- biden (score: 0.55)`
5830	`- respect reverence joe (score: 0.54)`
5831	`- reverence joe (score: 0.52)`
5832	`- biden person (score: 0.52)`
5833	`- newsom (score: 0.51)`
5834	`- deep respect reverence (score: 0.41)`
5835	`- joe (score: 0.41)`
5836	`- worthy conversation (score: 0.40)`
5837	`- character decency (score: 0.39)`
5838	`- respect reverence (score: 0.37)`
5839	`- things worthy conversation (score: 0.37)`
5840	`- person character decency (score: 0.36)`
5841	`- deep respect (score: 0.36)`
5842	`- character decency capacity (score: 0.36)`
5843	`Total keywords: 20 extracted in 0.0342 seconds`
5844
5845	`Dependency Relations (extracted in 0.0080sec):`
5846
5847	`Sentence 1: NEWSOM: I have deep respect, reverence for Joe Biden as a person, his character, his decency and his capacity to do great things.`
5848	`NEWSOM (NOUN) --[ROOT]--> NEWSOM (NOUN)`
5849	`: (PUNCT) --[punct]--> NEWSOM (NOUN)`
5850	`I (PRON) --[nsubj]--> have (AUX)`
5851	`have (AUX) --[acl]--> NEWSOM (NOUN)`
5852	`deep (ADJ) --[amod]--> respect (NOUN)`
5853	`respect (NOUN) --[dobj]--> have (AUX)`
5854	`, (PUNCT) --[punct]--> respect (NOUN)`
5855	`reverence (NOUN) --[conj]--> respect (NOUN)`
5856	`for (ADP) --[prep]--> reverence (NOUN)`
5857	`Joe (PROPN) --[compound]--> Biden (PROPN)`
5858	`Biden (PROPN) --[pobj]--> for (ADP)`
5859	`as (ADP) --[prep]--> have (AUX)`
5860	`a (DET) --[det]--> person (NOUN)`
5861	`person (NOUN) --[pobj]--> as (ADP)`
5862	`, (PUNCT) --[punct]--> person (NOUN)`
5863	`his (PRON) --[poss]--> character (NOUN)`
5864	`character (NOUN) --[conj]--> person (NOUN)`
5865	`, (PUNCT) --[punct]--> character (NOUN)`
5866	`his (PRON) --[poss]--> decency (NOUN)`
5867	`decency (NOUN) --[conj]--> character (NOUN)`
5868	`and (CCONJ) --[cc]--> decency (NOUN)`
5869	`his (PRON) --[poss]--> capacity (NOUN)`
5870	`capacity (NOUN) --[conj]--> decency (NOUN)`
5871	`to (PART) --[aux]--> do (VERB)`
5872	`do (VERB) --[relcl]--> character (NOUN)`
5873	`great (ADJ) --[amod]--> things (NOUN)`
5874	`things (NOUN) --[dobj]--> do (VERB)`
5875	`. (PUNCT) --[punct]--> have (AUX)`
5876
5877	`Sentence 2: That's why I'm not worthy of that conversation.`
5878	`That (PRON) --[nsubj]--> 's (AUX)`
5879	`'s (AUX) --[ROOT]--> 's (AUX)`
5880	`why (SCONJ) --[advmod]--> 'm (AUX)`
5881	`I (PRON) --[nsubj]--> 'm (AUX)`
5882	`'m (AUX) --[ccomp]--> 's (AUX)`
5883	`not (PART) --[neg]--> 'm (AUX)`
5884	`worthy (ADJ) --[acomp]--> 'm (AUX)`
5885	`of (ADP) --[prep]--> worthy (ADJ)`
5886	`that (DET) --[det]--> conversation (NOUN)`
5887	`conversation (NOUN) --[pobj]--> of (ADP)`
5888	`. (PUNCT) --[punct]--> 's (AUX)`
5889
5890	`Total sentences: 2`
5891
5892	`RAKE Keyphrase Relationships:`
5893	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
5894
5895	`YAKE Keyphrase Relationships:`
5896	`noun phrase: "deep respect" contains [deep respect], [respect], [deep]`
5897	`noun phrase: "Joe Biden" contains [joe biden], [joe], [biden]`
5898	`noun phrase: "great things" contains [great things], [things], [great]`
5899	`Total relationships found: 3`
5900
5901	`Timing Comparison:`
5902	`RAKE: 0.0000sec`
5903	`YAKE: 0.0180sec`
5904	`KeyBERT: 0.0342sec`
5905	`Dependencies: 0.0080sec`
5906	`Fastest: RAKE`
5907
5908	`================================================================================`
5909	`Message 46:`
5910	`SELYUKH: I think old habits die hard is another one. The word that's been coming up talking with economists is a reset. Like, we're resetting to a version of an even keel after the wild swings of the pandemic economy and, you know, sitting in traffic jams and parking lots in front of stores.`
5911	`--------------------------------------------------------------------------------`
5912	`RAKE Keywords:`
5913	`- wild swings (score: 4.00) → wild swing`
5914	`- traffic jams (score: 4.00) → traffic jam`
5915	`- parking lots (score: 4.00) → parking lot`
5916	`- pandemic economy (score: 4.00)`
5917	`- even keel (score: 4.00)`
5918	`- another one (score: 4.00)`
5919	`- word (score: 1.00)`
5920	`- version (score: 1.00)`
5921	`- talking (score: 1.00) → talk`
5922	`- stores (score: 1.00) → store`
5923	`- sitting (score: 1.00) → sit`
5924	`- selyukh (score: 1.00)`
5925	`- resetting (score: 1.00) → reset`
5926	`- reset (score: 1.00)`
5927	`- like (score: 1.00)`
5928	`- know (score: 1.00)`
5929	`- front (score: 1.00)`
5930	`- economists (score: 1.00) → economist`
5931	`- coming (score: 1.00) → come`
5932	`Total keywords: 19 extracted in 0.0000 seconds`
5933
5934	`YAKE Keywords:`
5935	`- habits die hard (score: 0.00) → habit die hard`
5936	`- habits die (score: 0.03) → habit die`
5937	`- die hard (score: 0.03)`
5938	`- SELYUKH (score: 0.04)`
5939	`- habits (score: 0.16) → habit`
5940	`- die (score: 0.16)`
5941	`- hard (score: 0.16)`
5942	`- coming up talking (score: 0.20) → come up talk`
5943	`- talking with economists (score: 0.20) → talk with economist`
5944	`- sitting in traffic (score: 0.24) → sit in traffic`
5945	`- front of stores (score: 0.24) → front of store`
5946	`- reset (score: 0.30)`
5947	`- wild swings (score: 0.33) → wild swing`
5948	`- pandemic economy (score: 0.33)`
5949	`- traffic jams (score: 0.33) → traffic jam`
5950	`- jams and parking (score: 0.33) → jam and parking`
5951	`- parking lots (score: 0.33) → parking lot`
5952	`- lots in front (score: 0.33) → lot in front`
5953	`- sitting (score: 0.39) → sit`
5954	`- stores (score: 0.39) → store`
5955	`Total keywords: 20 extracted in 0.0185 seconds`
5956
5957	`KeyBERT Keywords:`
5958	`- economists reset like (score: 0.72)`
5959	`- talking economists reset (score: 0.70)`
5960	`- economists reset (score: 0.68)`
5961	`- old habits die (score: 0.55)`
5962	`- swings pandemic economy (score: 0.52)`
5963	`- old habits (score: 0.51)`
5964	`- coming talking economists (score: 0.51)`
5965	`- think old habits (score: 0.50)`
5966	`- talking economists (score: 0.49)`
5967	`- economists (score: 0.48)`
5968	`- habits die hard (score: 0.47)`
5969	`- pandemic economy (score: 0.47)`
5970	`- pandemic economy know (score: 0.46)`
5971	`- habits die (score: 0.43)`
5972	`- economy (score: 0.43)`
5973	`- economy know (score: 0.38)`
5974	`- like resetting (score: 0.38)`
5975	`- economy know sitting (score: 0.37)`
5976	`- reset like (score: 0.35)`
5977	`- habits (score: 0.35)`
5978	`Total keywords: 20 extracted in 0.0581 seconds`
5979
5980	`Dependency Relations (extracted in 0.0152sec):`
5981
5982	`Sentence 1: SELYUKH: I think old habits die hard is another one.`
5983	`SELYUKH (NOUN) --[ROOT]--> SELYUKH (NOUN)`
5984	`: (PUNCT) --[punct]--> SELYUKH (NOUN)`
5985	`I (PRON) --[nsubj]--> think (VERB)`
5986	`think (VERB) --[parataxis]--> SELYUKH (NOUN)`
5987	`old (ADJ) --[amod]--> habits (NOUN)`
5988	`habits (NOUN) --[nsubj]--> die (VERB)`
5989	`die (VERB) --[ccomp]--> think (VERB)`
5990	`hard (ADV) --[advmod]--> die (VERB)`
5991	`is (AUX) --[ccomp]--> think (VERB)`
5992	`another (DET) --[det]--> one (NUM)`
5993	`one (NUM) --[attr]--> is (AUX)`
5994	`. (PUNCT) --[punct]--> think (VERB)`
5995
5996	`Sentence 2: The word that's been coming up talking with economists is a reset.`
5997	`The (DET) --[det]--> word (NOUN)`
5998	`word (NOUN) --[nsubj]--> is (AUX)`
5999	`that (PRON) --[nsubjpass]--> coming (VERB)`
6000	`'s (AUX) --[auxpass]--> coming (VERB)`
6001	`been (AUX) --[aux]--> coming (VERB)`
6002	`coming (VERB) --[relcl]--> word (NOUN)`
6003	`up (ADP) --[prt]--> coming (VERB)`
6004	`talking (VERB) --[advcl]--> coming (VERB)`
6005	`with (ADP) --[prep]--> talking (VERB)`
6006	`economists (NOUN) --[pobj]--> with (ADP)`
6007	`is (AUX) --[ROOT]--> is (AUX)`
6008	`a (DET) --[det]--> reset (NOUN)`
6009	`reset (NOUN) --[attr]--> is (AUX)`
6010	`. (PUNCT) --[punct]--> is (AUX)`
6011
6012	`Sentence 3: Like, we're resetting to a version of an even keel after the wild swings of the pandemic economy and, you know, sitting in traffic jams and parking lots in front of stores.`
6013	`Like (INTJ) --[intj]--> resetting (VERB)`
6014	`, (PUNCT) --[punct]--> resetting (VERB)`
6015	`we (PRON) --[nsubj]--> resetting (VERB)`
6016	`'re (AUX) --[aux]--> resetting (VERB)`
6017	`resetting (VERB) --[ROOT]--> resetting (VERB)`
6018	`to (ADP) --[prep]--> resetting (VERB)`
6019	`a (DET) --[det]--> version (NOUN)`
6020	`version (NOUN) --[pobj]--> to (ADP)`
6021	`of (ADP) --[prep]--> version (NOUN)`
6022	`an (DET) --[det]--> keel (NOUN)`
6023	`even (ADJ) --[amod]--> keel (NOUN)`
6024	`keel (NOUN) --[pobj]--> of (ADP)`
6025	`after (ADP) --[prep]--> resetting (VERB)`
6026	`the (DET) --[det]--> swings (NOUN)`
6027	`wild (ADJ) --[amod]--> swings (NOUN)`
6028	`swings (NOUN) --[pobj]--> after (ADP)`
6029	`of (ADP) --[prep]--> swings (NOUN)`
6030	`the (DET) --[det]--> economy (NOUN)`
6031	`pandemic (ADJ) --[amod]--> economy (NOUN)`
6032	`economy (NOUN) --[pobj]--> of (ADP)`
6033	`and (CCONJ) --[cc]--> resetting (VERB)`
6034	`, (PUNCT) --[punct]--> resetting (VERB)`
6035	`you (PRON) --[nsubj]--> know (VERB)`
6036	`know (VERB) --[parataxis]--> resetting (VERB)`
6037	`, (PUNCT) --[punct]--> know (VERB)`
6038	`sitting (VERB) --[advcl]--> resetting (VERB)`
6039	`in (ADP) --[prep]--> sitting (VERB)`
6040	`traffic (NOUN) --[compound]--> jams (NOUN)`
6041	`jams (NOUN) --[pobj]--> in (ADP)`
6042	`and (CCONJ) --[cc]--> jams (NOUN)`
6043	`parking (NOUN) --[compound]--> lots (NOUN)`
6044	`lots (NOUN) --[conj]--> jams (NOUN)`
6045	`in (ADP) --[prep]--> sitting (VERB)`
6046	`front (NOUN) --[pobj]--> in (ADP)`
6047	`of (ADP) --[prep]--> front (NOUN)`
6048	`stores (NOUN) --[pobj]--> of (ADP)`
6049	`. (PUNCT) --[punct]--> resetting (VERB)`
6050
6051	`Total sentences: 3`
6052
6053	`RAKE Keyphrase Relationships:`
6054	`verb phrase: "resetting 're to after" contains [resetting], [reset]`
6055	`Total relationships found: 1`
6056
6057	`YAKE Keyphrase Relationships:`
6058	`verb phrase: "die hard" contains [die hard], [die], [hard]`
6059	`Total relationships found: 1`
6060
6061	`Timing Comparison:`
6062	`RAKE: 0.0000sec`
6063	`YAKE: 0.0185sec`
6064	`KeyBERT: 0.0581sec`
6065	`Dependencies: 0.0152sec`
6066	`Fastest: RAKE`
6067
6068	`================================================================================`
6069	`Message 47:`
6070	`DORTE MURCH HANSEN: (Speaking Danish).`
6071	`--------------------------------------------------------------------------------`
6072	`RAKE Keywords:`
6073	`- speaking danish ). (score: 9.00)`
6074	`- dorte murch hansen (score: 9.00) → DORTE MURCH HANSEN`
6075	`Total keywords: 2 extracted in 0.0000 seconds`
6076
6077	`YAKE Keywords:`
6078	`- DORTE MURCH HANSEN (score: 0.00) → DORTE MURCH HANSEN`
6079	`- Speaking Danish (score: 0.01) → speak Danish`
6080	`- DORTE MURCH (score: 0.01) → DORTE MURCH`
6081	`- MURCH HANSEN (score: 0.01) → MURCH HANSEN`
6082	`- DORTE (score: 0.09) → DORTE`
6083	`- HANSEN (score: 0.09) → HANSEN`
6084	`- Speaking (score: 0.09) → speak`
6085	`- Danish (score: 0.09) → Danish`
6086	`- MURCH (score: 0.14) → MURCH`
6087	`Total keywords: 9 extracted in 0.0027 seconds`
6088
6089	`KeyBERT Keywords:`
6090	`- dorte murch hansen (score: 0.85)`
6091	`- hansen speaking danish (score: 0.81)`
6092	`- murch hansen (score: 0.77)`
6093	`- murch hansen speaking (score: 0.75)`
6094	`- speaking danish (score: 0.67)`
6095	`- danish (score: 0.65)`
6096	`- dorte murch (score: 0.64)`
6097	`- hansen speaking (score: 0.62)`
6098	`- hansen (score: 0.60)`
6099	`- dorte (score: 0.49)`
6100	`- murch (score: 0.49)`
6101	`- speaking (score: 0.23)`
6102	`Total keywords: 12 extracted in 0.0194 seconds`
6103
6104	`Dependency Relations (extracted in 0.0020sec):`
6105
6106	`Sentence 1: DORTE MURCH HANSEN: (Speaking Danish).`
6107	`DORTE (PROPN) --[compound]--> MURCH (PROPN)`
6108	`MURCH (PROPN) --[compound]--> HANSEN (PROPN)`
6109	`HANSEN (PROPN) --[ROOT]--> HANSEN (PROPN)`
6110	`: (PUNCT) --[punct]--> HANSEN (PROPN)`
6111	`( (PUNCT) --[punct]--> HANSEN (PROPN)`
6112	`Speaking (VERB) --[compound]--> Danish (PROPN)`
6113	`Danish (PROPN) --[appos]--> HANSEN (PROPN)`
6114	`) (PUNCT) --[punct]--> HANSEN (PROPN)`
6115	`. (PUNCT) --[punct]--> HANSEN (PROPN)`
6116
6117	`Total sentences: 1`
6118
6119	`RAKE Keyphrase Relationships:`
6120	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
6121
6122	`YAKE Keyphrase Relationships:`
6123	`noun phrase: "DORTE MURCH HANSEN" contains [dorte murch hansen], [dorte murch], [murch hansen], [dorte], [hansen], [murch]`
6124	`noun phrase: "Speaking Danish" contains [speaking danish], [speaking], [danish]`
6125	`Total relationships found: 2`
6126
6127	`Timing Comparison:`
6128	`RAKE: 0.0000sec`
6129	`YAKE: 0.0027sec`
6130	`KeyBERT: 0.0194sec`
6131	`Dependencies: 0.0020sec`
6132	`Fastest: RAKE`
6133
6134	`================================================================================`
6135	`Message 48:`
6136	`ANDRE: Not directly intentional. A lot of - we filmed the majority of the movie in Atlanta, you know, and around Georgia.`
6137	`--------------------------------------------------------------------------------`
6138	`RAKE Keywords:`
6139	`- directly intentional (score: 4.00)`
6140	`- around georgia (score: 4.00) → around Georgia`
6141	`- movie (score: 1.00)`
6142	`- majority (score: 1.00)`
6143	`- lot (score: 1.00)`
6144	`- know (score: 1.00)`
6145	`- filmed (score: 1.00) → film`
6146	`- atlanta (score: 1.00) → Atlanta`
6147	`- andre (score: 1.00) → ANDRE`
6148	`Total keywords: 9 extracted in 0.0000 seconds`
6149
6150	`YAKE Keywords:`
6151	`- directly intentional (score: 0.03)`
6152	`- ANDRE (score: 0.04) → ANDRE`
6153	`- movie in Atlanta (score: 0.10) → movie in Atlanta`
6154	`- intentional (score: 0.12)`
6155	`- Atlanta (score: 0.20) → Atlanta`
6156	`- Georgia (score: 0.20) → Georgia`
6157	`- directly (score: 0.20)`
6158	`- filmed the majority (score: 0.28) → film the majority`
6159	`- lot (score: 0.47)`
6160	`- filmed (score: 0.47) → film`
6161	`- majority (score: 0.47)`
6162	`- movie (score: 0.47)`
6163	`Total keywords: 12 extracted in 0.0040 seconds`
6164
6165	`KeyBERT Keywords:`
6166	`- intentional lot filmed (score: 0.59)`
6167	`- andre directly intentional (score: 0.58)`
6168	`- filmed (score: 0.56)`
6169	`- movie atlanta (score: 0.54)`
6170	`- movie atlanta know (score: 0.54)`
6171	`- directly intentional (score: 0.50)`
6172	`- atlanta know georgia (score: 0.47)`
6173	`- intentional (score: 0.45)`
6174	`- majority movie atlanta (score: 0.45)`
6175	`- lot filmed (score: 0.44)`
6176	`- atlanta (score: 0.43)`
6177	`- andre directly (score: 0.41)`
6178	`- filmed majority movie (score: 0.41)`
6179	`- know georgia (score: 0.40)`
6180	`- georgia (score: 0.40)`
6181	`- atlanta know (score: 0.39)`
6182	`- filmed majority (score: 0.39)`
6183	`- andre (score: 0.38)`
6184	`- directly intentional lot (score: 0.37)`
6185	`- intentional lot (score: 0.36)`
6186	`Total keywords: 20 extracted in 0.0198 seconds`
6187
6188	`Dependency Relations (extracted in 0.0076sec):`
6189
6190	`Sentence 1: ANDRE: Not directly intentional.`
6191	`ANDRE (PROPN) --[ROOT]--> ANDRE (PROPN)`
6192	`: (PUNCT) --[punct]--> ANDRE (PROPN)`
6193	`Not (PART) --[neg]--> directly (ADV)`
6194	`directly (ADV) --[advmod]--> intentional (ADJ)`
6195	`intentional (ADJ) --[amod]--> ANDRE (PROPN)`
6196	`. (PUNCT) --[punct]--> ANDRE (PROPN)`
6197
6198	`Sentence 2: A lot of - we filmed the majority of the movie in Atlanta, you know, and around Georgia.`
6199	`A (DET) --[det]--> lot (NOUN)`
6200	`lot (NOUN) --[npadvmod]--> filmed (VERB)`
6201	`of (ADP) --[prep]--> lot (NOUN)`
6202	`- (PUNCT) --[punct]--> of (ADP)`
6203	`we (PRON) --[nsubj]--> filmed (VERB)`
6204	`filmed (VERB) --[ROOT]--> filmed (VERB)`
6205	`the (DET) --[det]--> majority (NOUN)`
6206	`majority (NOUN) --[dobj]--> filmed (VERB)`
6207	`of (ADP) --[prep]--> majority (NOUN)`
6208	`the (DET) --[det]--> movie (NOUN)`
6209	`movie (NOUN) --[pobj]--> of (ADP)`
6210	`in (ADP) --[prep]--> movie (NOUN)`
6211	`Atlanta (PROPN) --[pobj]--> in (ADP)`
6212	`, (PUNCT) --[punct]--> majority (NOUN)`
6213	`you (PRON) --[nsubj]--> know (VERB)`
6214	`know (VERB) --[parataxis]--> filmed (VERB)`
6215	`, (PUNCT) --[punct]--> know (VERB)`
6216	`and (CCONJ) --[cc]--> filmed (VERB)`
6217	`around (ADP) --[conj]--> filmed (VERB)`
6218	`Georgia (PROPN) --[pobj]--> around (ADP)`
6219	`. (PUNCT) --[punct]--> filmed (VERB)`
6220
6221	`Total sentences: 2`
6222
6223	`RAKE Keyphrase Relationships:`
6224	`verb phrase: "filmed majority" contains [majority], [filmed]`
6225	`Total relationships found: 1`
6226
6227	`YAKE Keyphrase Relationships:`
6228	`verb phrase: "filmed majority" contains [filmed], [majority]`
6229	`Total relationships found: 1`
6230
6231	`Timing Comparison:`
6232	`RAKE: 0.0000sec`
6233	`YAKE: 0.0040sec`
6234	`KeyBERT: 0.0198sec`
6235	`Dependencies: 0.0076sec`
6236	`Fastest: RAKE`
6237
6238	`================================================================================`
6239	`Message 49:`
6240	`BLAIR: "Slow Horses" lets us in on the source of Lamb's misery gradually. He was a Cold War spy who saw death up close. Despite the reputation of his misfit spies, they're still part of British intelligence. They just don't get the sexy James Bond-style assignments. When they do get out, Lamb is on their case, like in this scene when they're looking for a hostage and his kidnappers in the woods.(SOUNDBITE OF TV SHOW, "SLOW HORSES")`
6241	`--------------------------------------------------------------------------------`
6242	`RAKE Keywords:`
6243	`- sexy james bond (score: 9.00) → sexy James Bond`
6244	`- cold war spy (score: 9.00) → Cold War spy`
6245	`- slow horses ") (score: 8.00)`
6246	`- slow horses (score: 5.00) → slow Horses`
6247	`- tv show (score: 4.00) → TV SHOW`
6248	`- style assignments (score: 4.00) → style assignment`
6249	`- still part (score: 4.00)`
6250	`- saw death (score: 4.00) → see death`
6251	`- misfit spies (score: 4.00) → misfit spy`
6252	`- misery gradually (score: 4.00)`
6253	`- lets us (score: 4.00) → let we`
6254	`- british intelligence (score: 4.00)`
6255	`- woods (score: 1.00)`
6256	`- source (score: 1.00)`
6257	`- soundbite (score: 1.00)`
6258	`- scene (score: 1.00)`
6259	`- reputation (score: 1.00)`
6260	`- looking (score: 1.00) → look`
6261	`- like (score: 1.00)`
6262	`- lamb (score: 1.00) → Lamb`
6263	`- lamb (score: 1.00) → Lamb`
6264	`- kidnappers (score: 1.00) → kidnapper`
6265	`- hostage (score: 1.00)`
6266	`- get (score: 1.00)`
6267	`- get (score: 1.00)`
6268	`- despite (score: 1.00)`
6269	`- close (score: 1.00)`
6270	`- case (score: 1.00)`
6271	`- blair (score: 1.00) → BLAIR`
6272	`Total keywords: 29 extracted in 0.0000 seconds`
6273
6274	`YAKE Keywords:`
6275	`- Lamb misery gradually (score: 0.01)`
6276	`- Slow Horses (score: 0.03) → slow Horses`
6277	`- misery gradually (score: 0.03)`
6278	`- Cold War spy (score: 0.03) → Cold War spy`
6279	`- BLAIR (score: 0.05) → BLAIR`
6280	`- Cold War (score: 0.06) → Cold War`
6281	`- Lamb misery (score: 0.06)`
6282	`- James Bond-style assignments (score: 0.10)`
6283	`- Slow (score: 0.11)`
6284	`- Horses (score: 0.11) → Horses`
6285	`- War spy (score: 0.12) → War spy`
6286	`- sexy James Bond-style (score: 0.12)`
6287	`- source of Lamb (score: 0.13) → source of Lamb`
6288	`- gradually (score: 0.14)`
6289	`- James Bond-style (score: 0.14)`
6290	`- Lamb (score: 0.16) → Lamb`
6291	`- British intelligence (score: 0.16)`
6292	`- source (score: 0.19)`
6293	`- misery (score: 0.19)`
6294	`- death up close (score: 0.19)`
6295	`Total keywords: 20 extracted in 0.0237 seconds`
6296
6297	`KeyBERT Keywords:`
6298	`- blair slow horses (score: 0.76)`
6299	`- blair slow (score: 0.61)`
6300	`- slow horses (score: 0.58)`
6301	`- slow horses lets (score: 0.52)`
6302	`- tv slow horses (score: 0.52)`
6303	`- blair (score: 0.48)`
6304	`- horses (score: 0.45)`
6305	`- lamb misery gradually (score: 0.44)`
6306	`- lamb misery (score: 0.43)`
6307	`- lamb (score: 0.41)`
6308	`- source lamb misery (score: 0.41)`
6309	`- spies british intelligence (score: 0.40)`
6310	`- horses lets source (score: 0.40)`
6311	`- british intelligence just (score: 0.39)`
6312	`- british intelligence (score: 0.38)`
6313	`- spies british (score: 0.38)`
6314	`- source lamb (score: 0.38)`
6315	`- horses lets (score: 0.37)`
6316	`- lamb case (score: 0.36)`
6317	`- james bond (score: 0.35)`
6318	`Total keywords: 20 extracted in 0.0706 seconds`
6319
6320	`Dependency Relations (extracted in 0.0095sec):`
6321
6322	`Sentence 1: BLAIR: "Slow Horses" lets us in on the source of Lamb's misery gradually.`
6323	`BLAIR (PROPN) --[npadvmod]--> lets (VERB)`
6324	`: (PUNCT) --[punct]--> BLAIR (PROPN)`
6325	`" (PUNCT) --[punct]--> BLAIR (PROPN)`
6326	`Slow (ADJ) --[amod]--> Horses (PROPN)`
6327	`Horses (PROPN) --[nsubj]--> lets (VERB)`
6328	`" (PUNCT) --[punct]--> Horses (PROPN)`
6329	`lets (VERB) --[ROOT]--> lets (VERB)`
6330	`us (PRON) --[dobj]--> lets (VERB)`
6331	`in (ADP) --[prt]--> lets (VERB)`
6332	`on (ADP) --[prep]--> lets (VERB)`
6333	`the (DET) --[det]--> source (NOUN)`
6334	`source (NOUN) --[pobj]--> on (ADP)`
6335	`of (ADP) --[prep]--> source (NOUN)`
6336	`Lamb (PROPN) --[poss]--> misery (NOUN)`
6337	`'s (PART) --[case]--> Lamb (PROPN)`
6338	`misery (NOUN) --[pobj]--> of (ADP)`
6339	`gradually (ADV) --[advmod]--> lets (VERB)`
6340	`. (PUNCT) --[punct]--> lets (VERB)`
6341
6342	`Sentence 2: He was a Cold War spy who saw death up close.`
6343	`He (PRON) --[nsubj]--> was (AUX)`
6344	`was (AUX) --[ROOT]--> was (AUX)`
6345	`a (DET) --[det]--> spy (NOUN)`
6346	`Cold (PROPN) --[compound]--> War (PROPN)`
6347	`War (PROPN) --[compound]--> spy (NOUN)`
6348	`spy (NOUN) --[attr]--> was (AUX)`
6349	`who (PRON) --[nsubj]--> saw (VERB)`
6350	`saw (VERB) --[relcl]--> spy (NOUN)`
6351	`death (NOUN) --[dobj]--> saw (VERB)`
6352	`up (ADP) --[advmod]--> close (ADV)`
6353	`close (ADV) --[advmod]--> saw (VERB)`
6354	`. (PUNCT) --[punct]--> was (AUX)`
6355
6356	`Sentence 3: Despite the reputation of his misfit spies, they're still part of British intelligence.`
6357	`Despite (SCONJ) --[prep]--> 're (AUX)`
6358	`the (DET) --[det]--> reputation (NOUN)`
6359	`reputation (NOUN) --[pobj]--> Despite (SCONJ)`
6360	`of (ADP) --[prep]--> reputation (NOUN)`
6361	`his (PRON) --[poss]--> spies (NOUN)`
6362	`misfit (ADJ) --[compound]--> spies (NOUN)`
6363	`spies (NOUN) --[pobj]--> of (ADP)`
6364	`, (PUNCT) --[punct]--> 're (AUX)`
6365	`they (PRON) --[nsubj]--> 're (AUX)`
6366	`'re (AUX) --[ROOT]--> 're (AUX)`
6367	`still (ADV) --[advmod]--> 're (AUX)`
6368	`part (NOUN) --[attr]--> 're (AUX)`
6369	`of (ADP) --[prep]--> part (NOUN)`
6370	`British (ADJ) --[amod]--> intelligence (NOUN)`
6371	`intelligence (NOUN) --[pobj]--> of (ADP)`
6372	`. (PUNCT) --[punct]--> 're (AUX)`
6373
6374	`Sentence 4: They just don't get the sexy James Bond-style assignments.`
6375	`They (PRON) --[nsubj]--> get (VERB)`
6376	`just (ADV) --[advmod]--> get (VERB)`
6377	`do (AUX) --[aux]--> get (VERB)`
6378	`n't (PART) --[neg]--> get (VERB)`
6379	`get (VERB) --[ROOT]--> get (VERB)`
6380	`the (DET) --[det]--> assignments (NOUN)`
6381	`sexy (ADJ) --[amod]--> assignments (NOUN)`
6382	`James (PROPN) --[compound]--> Bond (PROPN)`
6383	`Bond (PROPN) --[compound]--> style (NOUN)`
6384	`- (PUNCT) --[punct]--> style (NOUN)`
6385	`style (NOUN) --[compound]--> assignments (NOUN)`
6386	`assignments (NOUN) --[dobj]--> get (VERB)`
6387	`. (PUNCT) --[punct]--> get (VERB)`
6388
6389	`Sentence 5: When they do get out, Lamb is on their case, like in this scene when they're looking for a hostage and his kidnappers in the woods.(SOUNDBITE OF TV SHOW, "SLOW HORSES")`
6390	`When (SCONJ) --[advmod]--> get (VERB)`
6391	`they (PRON) --[nsubj]--> get (VERB)`
6392	`do (AUX) --[aux]--> get (VERB)`
6393	`get (VERB) --[advcl]--> is (AUX)`
6394	`out (ADP) --[prt]--> get (VERB)`
6395	`, (PUNCT) --[punct]--> is (AUX)`
6396	`Lamb (PROPN) --[nsubj]--> is (AUX)`
6397	`is (AUX) --[ROOT]--> is (AUX)`
6398	`on (ADP) --[prep]--> is (AUX)`
6399	`their (PRON) --[poss]--> case (NOUN)`
6400	`case (NOUN) --[pobj]--> on (ADP)`
6401	`, (PUNCT) --[punct]--> is (AUX)`
6402	`like (ADP) --[prep]--> is (AUX)`
6403	`in (ADP) --[prep]--> like (ADP)`
6404	`this (DET) --[det]--> scene (NOUN)`
6405	`scene (NOUN) --[pobj]--> in (ADP)`
6406	`when (SCONJ) --[advmod]--> looking (VERB)`
6407	`they (PRON) --[nsubj]--> looking (VERB)`
6408	`'re (AUX) --[aux]--> looking (VERB)`
6409	`looking (VERB) --[advcl]--> is (AUX)`
6410	`for (ADP) --[prep]--> looking (VERB)`
6411	`a (DET) --[det]--> hostage (NOUN)`
6412	`hostage (NOUN) --[pobj]--> for (ADP)`
6413	`and (CCONJ) --[cc]--> hostage (NOUN)`
6414	`his (PRON) --[poss]--> kidnappers (NOUN)`
6415	`kidnappers (NOUN) --[conj]--> hostage (NOUN)`
6416	`in (ADP) --[prep]--> kidnappers (NOUN)`
6417	`the (DET) --[det]--> woods.(SOUNDBITE (NOUN)`
6418	`woods.(SOUNDBITE (NOUN) --[pobj]--> in (ADP)`
6419	`OF (ADP) --[prep]--> woods.(SOUNDBITE (NOUN)`
6420	`TV (PROPN) --[compound]--> SHOW (PROPN)`
6421	`SHOW (PROPN) --[pobj]--> OF (ADP)`
6422	`, (PUNCT) --[punct]--> is (AUX)`
6423	`" (PUNCT) --[punct]--> HORSES (NOUN)`
6424	`SLOW (ADJ) --[amod]--> HORSES (NOUN)`
6425	`HORSES (NOUN) --[attr]--> is (AUX)`
6426	`" (PUNCT) --[punct]--> HORSES (NOUN)`
6427	`) (PUNCT) --[punct]--> is (AUX)`
6428
6429	`Total sentences: 5`
6430
6431	`RAKE Keyphrase Relationships:`
6432	`noun phrase: "Lamb's misery" contains [lamb], [lamb]`
6433	`noun phrase: "the sexy James Bond-style assignments" contains [sexy james bond], [style assignments]`
6434	`noun phrase: "Lamb" contains [lamb], [lamb]`
6435	`noun phrase: "the woods.(SOUNDBITE" contains [woods], [soundbite]`
6436	`verb phrase: "saw death close" contains [saw death], [close]`
6437	`verb phrase: "get just do n't assignments" contains [get], [get]`
6438	`verb phrase: "get When do" contains [get], [get]`
6439	`Total relationships found: 7`
6440
6441	`YAKE Keyphrase Relationships:`
6442	`noun phrase: "Slow Horses" contains [slow horses], [slow], [horses]`
6443	`noun phrase: "Lamb's misery" contains [lamb], [misery]`
6444	`noun phrase: "a Cold War spy" contains [cold war spy], [cold war], [war spy]`
6445	`noun phrase: "the sexy James Bond-style assignments" contains [james bond-style assignments], [sexy james bond-style], [james bond-style]`
6446	`noun phrase: ""SLOW HORSES" contains [slow horses], [slow], [horses]`
6447	`Total relationships found: 5`
6448
6449	`Timing Comparison:`
6450	`RAKE: 0.0000sec`
6451	`YAKE: 0.0237sec`
6452	`KeyBERT: 0.0706sec`
6453	`Dependencies: 0.0095sec`
6454	`Fastest: RAKE`
6455
6456	`================================================================================`
6457	`Message 50:`
6458	`WHEELBARGER: Thank you.`
6459	`--------------------------------------------------------------------------------`
6460	`RAKE Keywords:`
6461	`- wheelbarger (score: 1.00)`
6462	`- thank (score: 1.00)`
6463	`Total keywords: 2 extracted in 0.0000 seconds`
6464
6465	`YAKE Keywords:`
6466	`- WHEELBARGER (score: 0.03)`
6467	`Total keywords: 1 extracted in 0.0000 seconds`
6468
6469	`KeyBERT Keywords:`
6470	`- wheelbarger thank (score: 0.89)`
6471	`- wheelbarger (score: 0.82)`
6472	`- thank (score: 0.26)`
6473	`Total keywords: 3 extracted in 0.0138 seconds`
6474
6475	`Dependency Relations (extracted in 0.0040sec):`
6476
6477	`Sentence 1: WHEELBARGER:`
6478	`WHEELBARGER (NOUN) --[ROOT]--> WHEELBARGER (NOUN)`
6479	`: (PUNCT) --[punct]--> WHEELBARGER (NOUN)`
6480
6481	`Sentence 2: Thank you.`
6482	`Thank (VERB) --[ROOT]--> Thank (VERB)`
6483	`you (PRON) --[dobj]--> Thank (VERB)`
6484	`. (PUNCT) --[punct]--> Thank (VERB)`
6485
6486	`Total sentences: 2`
6487
6488	`RAKE Keyphrase Relationships:`
6489	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
6490
6491	`YAKE Keyphrase Relationships:`
6492	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
6493
6494	`Timing Comparison:`
6495	`RAKE: 0.0000sec`
6496	`YAKE: 0.0000sec`
6497	`KeyBERT: 0.0138sec`
6498	`Dependencies: 0.0040sec`
6499	`Fastest: RAKE`
6500
6501	`================================================================================`
6502	`Message 51:`
6503	WEINGART: A decade and a half later, in 1963, that was the weakness of the law that a young member of the American Nazi Party targeted. His name was Alexander DeFields. He moved to South Bend, Ind., from Michigan in a strategic move to organize for the American Nazi Party just as the civil rights movement was heating up. That year, he distributed anti-Semitic and racist leaflets for the party on cars outside of a bowling alley. He was later arrested for violating the Indiana anti-hate act. The case had been largely lost to history. Libby Baker and Judy Shroyer of the Michiana Jewish Historical Society came upon the case.
6504	`--------------------------------------------------------------------------------`
6505	`RAKE Keywords:`
6506	`- civil rights movement (score: 9.00) → civil right movement`
6507	`- american nazi party (score: 8.00) → American Nazi Party`
6508	`- young member (score: 4.00)`
6509	`- strategic move (score: 4.00)`
6510	`- south bend (score: 4.00) → South Bend`
6511	`- racist leaflets (score: 4.00) → racist leaflet`
6512	`- libby baker (score: 4.00) → Libby Baker`
6513	`- later arrested (score: 4.00) → later arrest`
6514	`- largely lost (score: 4.00) → largely lose`
6515	`- judy shroyer (score: 4.00) → Judy Shroyer`
6516	`- indiana anti (score: 4.00) → Indiana anti`
6517	`- ind ., (score: 4.00)`
6518	`- hate act (score: 4.00)`
6519	`- half later (score: 4.00)`
6520	`- distributed anti (score: 4.00) → distribute anti`
6521	`- cars outside (score: 4.00) → car outside`
6522	`- bowling alley (score: 4.00)`
6523	`- alexander defields (score: 4.00) → Alexander DeFields`
6524	`- party (score: 2.00) → Party`
6525	`- year (score: 1.00)`
6526	`- weingart (score: 1.00) → WEINGART`
6527	`- weakness (score: 1.00)`
6528	`- violating (score: 1.00) → violate`
6529	`- semitic (score: 1.00)`
6530	`- organize (score: 1.00)`
6531	`- name (score: 1.00)`
6532	`- moved (score: 1.00) → move`
6533	`- michigan (score: 1.00) → Michigan`
6534	`- law (score: 1.00)`
6535	`- history (score: 1.00)`
6536	`- heating (score: 1.00) → heat`
6537	`- decade (score: 1.00)`
6538	`- case (score: 1.00)`
6539	`- case (score: 1.00)`
6540	`- 1963 (score: 1.00)`
6541	`Total keywords: 35 extracted in 0.0000 seconds`
6542
6543	`YAKE Keywords:`
6544	`- American Nazi Party (score: 0.00) → American Nazi Party`
6545	`- Nazi Party targeted (score: 0.00) → Nazi Party target`
6546	`- American Nazi (score: 0.01) → American Nazi`
6547	`- Nazi Party (score: 0.01) → Nazi Party`
6548	`- young member (score: 0.02)`
6549	`- Party targeted (score: 0.03) → Party target`
6550	`- WEINGART (score: 0.05) → WEINGART`
6551	`- South Bend (score: 0.06) → South Bend`
6552	`- Alexander DeFields (score: 0.06) → Alexander DeFields`
6553	`- Michiana Jewish Historical (score: 0.07) → Michiana Jewish Historical`
6554	`- Jewish Historical Society (score: 0.07) → Jewish Historical Society`
6555	`- American (score: 0.07) → American`
6556	`- Nazi (score: 0.07) → Nazi`
6557	`- Party (score: 0.07) → Party`
6558	`- targeted (score: 0.12) → target`
6559	`- moved to South (score: 0.13) → move to South`
6560	`- decade (score: 0.14)`
6561	`- half (score: 0.14)`
6562	`- weakness (score: 0.14)`
6563	`- law (score: 0.14)`
6564	`Total keywords: 20 extracted in 0.0252 seconds`
6565
6566	`KeyBERT Keywords:`
6567	`- weingart (score: 0.48)`
6568	`- nazi party targeted (score: 0.44)`
6569	`- weingart decade (score: 0.42)`
6570	`- member american nazi (score: 0.41)`
6571	`- weingart decade half (score: 0.41)`
6572	`- later arrested violating (score: 0.40)`
6573	`- american nazi party (score: 0.40)`
6574	`- american nazi (score: 0.39)`
6575	`- alexander defields moved (score: 0.38)`
6576	`- jewish historical (score: 0.38)`
6577	`- semitic racist leaflets (score: 0.38)`
6578	`- party targeted alexander (score: 0.38)`
6579	`- later 1963 (score: 0.37)`
6580	`- history libby (score: 0.37)`
6581	`- shroyer michiana jewish (score: 0.37)`
6582	`- alexander defields (score: 0.37)`
6583	`- organize american nazi (score: 0.36)`
6584	`- nazi party (score: 0.36)`
6585	`- racist leaflets (score: 0.36)`
6586	`- michiana jewish historical (score: 0.36)`
6587	`Total keywords: 20 extracted in 0.1127 seconds`
6588
6589	`Dependency Relations (extracted in 0.0239sec):`
6590
6591	`Sentence 1: WEINGART: A decade and a half later, in 1963, that was the weakness of the law that a young member of the American Nazi Party targeted.`
6592	`WEINGART (PROPN) --[dep]--> was (AUX)`
6593	`: (PUNCT) --[punct]--> WEINGART (PROPN)`
6594	`A (DET) --[det]--> decade (NOUN)`
6595	`decade (NOUN) --[appos]--> WEINGART (PROPN)`
6596	`and (CCONJ) --[cc]--> decade (NOUN)`
6597	`a (DET) --[det]--> half (NOUN)`
6598	`half (NOUN) --[npadvmod]--> later (ADV)`
6599	`later (ADV) --[conj]--> decade (NOUN)`
6600	`, (PUNCT) --[punct]--> was (AUX)`
6601	`in (ADP) --[prep]--> was (AUX)`
6602	`1963 (NUM) --[pobj]--> in (ADP)`
6603	`, (PUNCT) --[punct]--> was (AUX)`
6604	`that (PRON) --[nsubj]--> was (AUX)`
6605	`was (AUX) --[ROOT]--> was (AUX)`
6606	`the (DET) --[det]--> weakness (NOUN)`
6607	`weakness (NOUN) --[attr]--> was (AUX)`
6608	`of (ADP) --[prep]--> weakness (NOUN)`
6609	`the (DET) --[det]--> law (NOUN)`
6610	`law (NOUN) --[pobj]--> of (ADP)`
6611	`that (PRON) --[dobj]--> targeted (VERB)`
6612	`a (DET) --[det]--> member (NOUN)`
6613	`young (ADJ) --[amod]--> member (NOUN)`
6614	`member (NOUN) --[nsubj]--> targeted (VERB)`
6615	`of (ADP) --[prep]--> member (NOUN)`
6616	`the (DET) --[det]--> Party (PROPN)`
6617	`American (PROPN) --[compound]--> Party (PROPN)`
6618	`Nazi (PROPN) --[compound]--> Party (PROPN)`
6619	`Party (PROPN) --[pobj]--> of (ADP)`
6620	`targeted (VERB) --[relcl]--> law (NOUN)`
6621	`. (PUNCT) --[punct]--> was (AUX)`
6622
6623	`Sentence 2: His name was Alexander DeFields.`
6624	`His (PRON) --[poss]--> name (NOUN)`
6625	`name (NOUN) --[nsubj]--> was (AUX)`
6626	`was (AUX) --[ROOT]--> was (AUX)`
6627	`Alexander (PROPN) --[compound]--> DeFields (PROPN)`
6628	`DeFields (PROPN) --[attr]--> was (AUX)`
6629	`. (PUNCT) --[punct]--> was (AUX)`
6630
6631	`Sentence 3: He moved to South Bend, Ind., from Michigan in a strategic move to organize for the American Nazi Party just as the civil rights movement was heating up.`
6632	`He (PRON) --[nsubj]--> moved (VERB)`
6633	`moved (VERB) --[ROOT]--> moved (VERB)`
6634	`to (ADP) --[prep]--> moved (VERB)`
6635	`South (PROPN) --[compound]--> Bend (PROPN)`
6636	`Bend (PROPN) --[pobj]--> to (ADP)`
6637	`, (PUNCT) --[punct]--> Bend (PROPN)`
6638	`Ind. (PROPN) --[appos]--> Bend (PROPN)`
6639	`, (PUNCT) --[punct]--> Bend (PROPN)`
6640	`from (ADP) --[prep]--> moved (VERB)`
6641	`Michigan (PROPN) --[pobj]--> from (ADP)`
6642	`in (ADP) --[prep]--> moved (VERB)`
6643	`a (DET) --[det]--> move (NOUN)`
6644	`strategic (ADJ) --[amod]--> move (NOUN)`
6645	`move (NOUN) --[pobj]--> in (ADP)`
6646	`to (PART) --[aux]--> organize (VERB)`
6647	`organize (VERB) --[relcl]--> move (NOUN)`
6648	`for (ADP) --[prep]--> organize (VERB)`
6649	`the (DET) --[det]--> Party (PROPN)`
6650	`American (PROPN) --[compound]--> Party (PROPN)`
6651	`Nazi (PROPN) --[compound]--> Party (PROPN)`
6652	`Party (PROPN) --[pobj]--> for (ADP)`
6653	`just (ADV) --[advmod]--> heating (VERB)`
6654	`as (SCONJ) --[mark]--> heating (VERB)`
6655	`the (DET) --[det]--> movement (NOUN)`
6656	`civil (ADJ) --[amod]--> rights (NOUN)`
6657	`rights (NOUN) --[compound]--> movement (NOUN)`
6658	`movement (NOUN) --[nsubj]--> heating (VERB)`
6659	`was (AUX) --[aux]--> heating (VERB)`
6660	`heating (VERB) --[advcl]--> moved (VERB)`
6661	`up (ADP) --[prt]--> heating (VERB)`
6662	`. (PUNCT) --[punct]--> moved (VERB)`
6663
6664	`Sentence 4: That year, he distributed anti-Semitic and racist leaflets for the party on cars outside of a bowling alley.`
6665	`That (DET) --[det]--> year (NOUN)`
6666	`year (NOUN) --[npadvmod]--> distributed (VERB)`
6667	`, (PUNCT) --[punct]--> distributed (VERB)`
6668	`he (PRON) --[nsubj]--> distributed (VERB)`
6669	`distributed (VERB) --[ROOT]--> distributed (VERB)`
6670	`anti (ADJ) --[dobj]--> distributed (VERB)`
6671	`- (ADJ) --[dobj]--> distributed (VERB)`
6672	`Semitic (ADJ) --[amod]--> leaflets (NOUN)`
6673	`and (CCONJ) --[cc]--> Semitic (ADJ)`
6674	`racist (ADJ) --[conj]--> Semitic (ADJ)`
6675	`leaflets (NOUN) --[dobj]--> distributed (VERB)`
6676	`for (ADP) --[prep]--> leaflets (NOUN)`
6677	`the (DET) --[det]--> party (NOUN)`
6678	`party (NOUN) --[pobj]--> for (ADP)`
6679	`on (ADP) --[prep]--> leaflets (NOUN)`
6680	`cars (NOUN) --[pobj]--> on (ADP)`
6681	`outside (ADP) --[prep]--> leaflets (NOUN)`
6682	`of (ADP) --[prep]--> outside (ADP)`
6683	`a (DET) --[det]--> alley (NOUN)`
6684	`bowling (NOUN) --[compound]--> alley (NOUN)`
6685	`alley (NOUN) --[pobj]--> of (ADP)`
6686	`. (PUNCT) --[punct]--> distributed (VERB)`
6687
6688	`Sentence 5: He was later arrested for violating the Indiana anti-hate act.`
6689	`He (PRON) --[nsubjpass]--> arrested (VERB)`
6690	`was (AUX) --[auxpass]--> arrested (VERB)`
6691	`later (ADV) --[advmod]--> arrested (VERB)`
6692	`arrested (VERB) --[ROOT]--> arrested (VERB)`
6693	`for (ADP) --[prep]--> arrested (VERB)`
6694	`violating (VERB) --[pcomp]--> for (ADP)`
6695	`the (DET) --[det]--> act (NOUN)`
6696	`Indiana (PROPN) --[compound]--> act (NOUN)`
6697	`anti (ADJ) --[amod]--> act (NOUN)`
6698	`- (ADJ) --[amod]--> act (NOUN)`
6699	`hate (ADJ) --[amod]--> act (NOUN)`
6700	`act (NOUN) --[dobj]--> violating (VERB)`
6701	`. (PUNCT) --[punct]--> arrested (VERB)`
6702
6703	`Sentence 6: The case had been largely lost to history.`
6704	`The (DET) --[det]--> case (NOUN)`
6705	`case (NOUN) --[nsubjpass]--> lost (VERB)`
6706	`had (AUX) --[aux]--> lost (VERB)`
6707	`been (AUX) --[auxpass]--> lost (VERB)`
6708	`largely (ADV) --[advmod]--> lost (VERB)`
6709	`lost (VERB) --[ROOT]--> lost (VERB)`
6710	`to (ADP) --[prep]--> lost (VERB)`
6711	`history (NOUN) --[pobj]--> to (ADP)`
6712	`. (PUNCT) --[punct]--> lost (VERB)`
6713
6714	`Sentence 7: Libby Baker and Judy Shroyer of the Michiana Jewish Historical Society came upon the case.`
6715	`Libby (PROPN) --[compound]--> Baker (PROPN)`
6716	`Baker (PROPN) --[nsubj]--> came (VERB)`
6717	`and (CCONJ) --[cc]--> Baker (PROPN)`
6718	`Judy (PROPN) --[compound]--> Shroyer (PROPN)`
6719	`Shroyer (PROPN) --[conj]--> Baker (PROPN)`
6720	`of (ADP) --[prep]--> Shroyer (PROPN)`
6721	`the (DET) --[det]--> Society (PROPN)`
6722	`Michiana (PROPN) --[nmod]--> Society (PROPN)`
6723	`Jewish (PROPN) --[compound]--> Society (PROPN)`
6724	`Historical (PROPN) --[compound]--> Society (PROPN)`
6725	`Society (PROPN) --[pobj]--> of (ADP)`
6726	`came (VERB) --[ROOT]--> came (VERB)`
6727	`upon (SCONJ) --[prep]--> came (VERB)`
6728	`the (DET) --[det]--> case (NOUN)`
6729	`case (NOUN) --[pobj]--> upon (SCONJ)`
6730	`. (PUNCT) --[punct]--> came (VERB)`
6731
6732	`Total sentences: 7`
6733
6734	`RAKE Keyphrase Relationships:`
6735	`noun phrase: "the American Nazi Party" contains [american nazi party], [party]`
6736	`noun phrase: "the American Nazi Party" contains [american nazi party], [party]`
6737	`noun phrase: "Semitic and racist leaflets" contains [racist leaflets], [semitic]`
6738	`noun phrase: "the Indiana anti-hate act" contains [indiana anti], [hate act]`
6739	`noun phrase: "The case" contains [case], [case]`
6740	`noun phrase: "the case" contains [case], [case]`
6741	`Total relationships found: 6`
6742
6743	`YAKE Keyphrase Relationships:`
6744	`noun phrase: "the American Nazi Party" contains [american nazi party], [american nazi], [nazi party], [american], [nazi], [party]`
6745	`noun phrase: "the American Nazi Party" contains [american nazi party], [american nazi], [nazi party], [american], [nazi], [party]`
6746	`noun phrase: "the Michiana Jewish Historical Society" contains [michiana jewish historical], [jewish historical society]`
6747	`Total relationships found: 3`
6748
6749	`Timing Comparison:`
6750	`RAKE: 0.0000sec`
6751	`YAKE: 0.0252sec`
6752	`KeyBERT: 0.1127sec`
6753	`Dependencies: 0.0239sec`
6754	`Fastest: RAKE`
6755
6756	`================================================================================`
6757	`Message 52:`
6758	MASKELL: Well, it's incredible the fact that you can, but it's never been tested because there has always been respect for our Parliament. And I think today has been a bad day for British democracy. The people of our country across our constituencies vote for us to hold the government to account, to scrutinize their actions, to ask questions and to debate. And by preventing us on the biggest issue facing Great Britain at this time - it's quite shocking that the prime minister is not wanting to be held to account and seek the counsel of his peers.
6759	`--------------------------------------------------------------------------------`
6760	`RAKE Keywords:`
6761	`- think today (score: 4.00)`
6762	`- quite shocking (score: 4.00)`
6763	`- prime minister (score: 4.00)`
6764	`- country across (score: 4.00)`
6765	`- constituencies vote (score: 4.00) → constituency vote`
6766	`- british democracy (score: 4.00)`
6767	`- bad day (score: 4.00)`
6768	`- ask questions (score: 4.00) → ask question`
6769	`- preventing us (score: 3.50) → prevent we`
6770	`- us (score: 1.50) → we`
6771	`- well (score: 1.00)`
6772	`- wanting (score: 1.00) → want`
6773	`- time (score: 1.00)`
6774	`- tested (score: 1.00) → test`
6775	`- seek (score: 1.00)`
6776	`- scrutinize (score: 1.00)`
6777	`- respect (score: 1.00)`
6778	`- people (score: 1.00)`
6779	`- peers (score: 1.00) → peer`
6780	`- parliament (score: 1.00) → Parliament`
6781	`- never (score: 1.00)`
6782	`- maskell (score: 1.00) → MASKELL`
6783	`- incredible (score: 1.00)`
6784	`- hold (score: 1.00)`
6785	`- held (score: 1.00) → hold`
6786	`- government (score: 1.00)`
6787	`- fact (score: 1.00)`
6788	`- debate (score: 1.00)`
6789	`- counsel (score: 1.00)`
6790	`- always (score: 1.00)`
6791	`- actions (score: 1.00) → action`
6792	`- account (score: 1.00)`
6793	`- account (score: 1.00)`
6794	`Total keywords: 33 extracted in 0.0000 seconds`
6795
6796	`YAKE Keywords:`
6797	`- incredible the fact (score: 0.02)`
6798	`- MASKELL (score: 0.04) → MASKELL`
6799	`- Parliament (score: 0.05) → Parliament`
6800	`- facing Great Britain (score: 0.05) → face Great Britain`
6801	`- British democracy (score: 0.05)`
6802	`- day for British (score: 0.07)`
6803	`- Great Britain (score: 0.09) → Great Britain`
6804	`- issue facing Great (score: 0.10) → issue face Great`
6805	`- bad day (score: 0.12)`
6806	`- incredible (score: 0.12)`
6807	`- fact (score: 0.12)`
6808	`- tested (score: 0.12) → test`
6809	`- respect (score: 0.12)`
6810	`- facing Great (score: 0.16) → face Great`
6811	`- government to account (score: 0.16)`
6812	`- account (score: 0.17)`
6813	`- scrutinize their actions (score: 0.18) → scrutinize their action`
6814	`- held to account (score: 0.19) → hold to account`
6815	`- British (score: 0.19)`
6816	`- biggest issue facing (score: 0.19) → big issue face`
6817	`Total keywords: 20 extracted in 0.0239 seconds`
6818
6819	`KeyBERT Keywords:`
6820	`- respect parliament (score: 0.61)`
6821	`- respect parliament think (score: 0.60)`
6822	`- tested respect parliament (score: 0.56)`
6823	`- parliament think today (score: 0.55)`
6824	`- british democracy (score: 0.52)`
6825	`- parliament think (score: 0.51)`
6826	`- day british democracy (score: 0.50)`
6827	`- british democracy people (score: 0.47)`
6828	`- parliament (score: 0.46)`
6829	`- facing great britain (score: 0.44)`
6830	`- prime minister wanting (score: 0.44)`
6831	`- minister wanting held (score: 0.43)`
6832	`- britain time quite (score: 0.39)`
6833	`- shocking prime minister (score: 0.39)`
6834	`- minister (score: 0.36)`
6835	`- prime minister (score: 0.36)`
6836	`- britain time (score: 0.35)`
6837	`- minister wanting (score: 0.35)`
6838	`- hold government account (score: 0.35)`
6839	`- hold government (score: 0.35)`
6840	`Total keywords: 20 extracted in 0.0772 seconds`
6841
6842	`Dependency Relations (extracted in 0.0133sec):`
6843
6844	`Sentence 1: MASKELL:`
6845	`MASKELL (PROPN) --[ROOT]--> MASKELL (PROPN)`
6846	`: (PUNCT) --[punct]--> MASKELL (PROPN)`
6847
6848	`Sentence 2: Well, it's incredible the fact that you can, but it's never been tested because there has always been respect for our Parliament.`
6849	`Well (INTJ) --[intj]--> 's (AUX)`
6850	`, (PUNCT) --[punct]--> 's (AUX)`
6851	`it (PRON) --[nsubj]--> 's (AUX)`
6852	`'s (AUX) --[ROOT]--> 's (AUX)`
6853	`incredible (ADJ) --[acomp]--> 's (AUX)`
6854	`the (DET) --[det]--> fact (NOUN)`
6855	`fact (NOUN) --[attr]--> 's (AUX)`
6856	`that (SCONJ) --[mark]--> can (AUX)`
6857	`you (PRON) --[nsubj]--> can (AUX)`
6858	`can (AUX) --[acl]--> fact (NOUN)`
6859	`, (PUNCT) --[punct]--> 's (AUX)`
6860	`but (CCONJ) --[cc]--> 's (AUX)`
6861	`it (PRON) --[nsubjpass]--> tested (VERB)`
6862	`'s (AUX) --[auxpass]--> tested (VERB)`
6863	`never (ADV) --[neg]--> tested (VERB)`
6864	`been (AUX) --[auxpass]--> tested (VERB)`
6865	`tested (VERB) --[conj]--> 's (AUX)`
6866	`because (SCONJ) --[mark]--> been (AUX)`
6867	`there (PRON) --[expl]--> been (AUX)`
6868	`has (AUX) --[aux]--> been (AUX)`
6869	`always (ADV) --[advmod]--> been (AUX)`
6870	`been (AUX) --[advcl]--> tested (VERB)`
6871	`respect (NOUN) --[attr]--> been (AUX)`
6872	`for (ADP) --[prep]--> respect (NOUN)`
6873	`our (PRON) --[poss]--> Parliament (PROPN)`
6874	`Parliament (PROPN) --[pobj]--> for (ADP)`
6875	`. (PUNCT) --[punct]--> tested (VERB)`
6876
6877	`Sentence 3: And I think today has been a bad day for British democracy.`
6878	`And (CCONJ) --[cc]--> think (VERB)`
6879	`I (PRON) --[nsubj]--> think (VERB)`
6880	`think (VERB) --[ROOT]--> think (VERB)`
6881	`today (NOUN) --[nsubj]--> been (AUX)`
6882	`has (AUX) --[aux]--> been (AUX)`
6883	`been (AUX) --[ccomp]--> think (VERB)`
6884	`a (DET) --[det]--> day (NOUN)`
6885	`bad (ADJ) --[amod]--> day (NOUN)`
6886	`day (NOUN) --[attr]--> been (AUX)`
6887	`for (ADP) --[prep]--> been (AUX)`
6888	`British (ADJ) --[amod]--> democracy (NOUN)`
6889	`democracy (NOUN) --[pobj]--> for (ADP)`
6890	`. (PUNCT) --[punct]--> think (VERB)`
6891
6892	`Sentence 4: The people of our country across our constituencies vote for us to hold the government to account, to scrutinize their actions, to ask questions and to debate.`
6893	`The (DET) --[det]--> people (NOUN)`
6894	`people (NOUN) --[nsubj]--> vote (VERB)`
6895	`of (ADP) --[prep]--> people (NOUN)`
6896	`our (PRON) --[poss]--> country (NOUN)`
6897	`country (NOUN) --[pobj]--> of (ADP)`
6898	`across (ADP) --[prep]--> people (NOUN)`
6899	`our (PRON) --[poss]--> constituencies (NOUN)`
6900	`constituencies (NOUN) --[pobj]--> across (ADP)`
6901	`vote (VERB) --[ROOT]--> vote (VERB)`
6902	`for (SCONJ) --[mark]--> hold (VERB)`
6903	`us (PRON) --[nsubj]--> hold (VERB)`
6904	`to (PART) --[aux]--> hold (VERB)`
6905	`hold (VERB) --[advcl]--> vote (VERB)`
6906	`the (DET) --[det]--> government (NOUN)`
6907	`government (NOUN) --[nsubj]--> account (VERB)`
6908	`to (PART) --[aux]--> account (VERB)`
6909	`account (VERB) --[ccomp]--> hold (VERB)`
6910	`, (PUNCT) --[punct]--> hold (VERB)`
6911	`to (PART) --[aux]--> scrutinize (VERB)`
6912	`scrutinize (VERB) --[advcl]--> hold (VERB)`
6913	`their (PRON) --[poss]--> actions (NOUN)`
6914	`actions (NOUN) --[dobj]--> scrutinize (VERB)`
6915	`, (PUNCT) --[punct]--> scrutinize (VERB)`
6916	`to (PART) --[aux]--> ask (VERB)`
6917	`ask (VERB) --[advcl]--> scrutinize (VERB)`
6918	`questions (NOUN) --[dobj]--> ask (VERB)`
6919	`and (CCONJ) --[cc]--> ask (VERB)`
6920	`to (PART) --[aux]--> debate (VERB)`
6921	`debate (VERB) --[conj]--> ask (VERB)`
6922	`. (PUNCT) --[punct]--> vote (VERB)`
6923
6924	`Sentence 5: And by preventing us on the biggest issue facing Great Britain at this time - it's quite shocking that the prime minister is not wanting to be held to account and seek the counsel of his peers.`
6925	`And (CCONJ) --[cc]--> 's (AUX)`
6926	`by (ADP) --[prep]--> 's (AUX)`
6927	`preventing (VERB) --[pcomp]--> by (ADP)`
6928	`us (PRON) --[dobj]--> preventing (VERB)`
6929	`on (ADP) --[prep]--> preventing (VERB)`
6930	`the (DET) --[det]--> issue (NOUN)`
6931	`biggest (ADJ) --[amod]--> issue (NOUN)`
6932	`issue (NOUN) --[pobj]--> on (ADP)`
6933	`facing (VERB) --[acl]--> issue (NOUN)`
6934	`Great (PROPN) --[compound]--> Britain (PROPN)`
6935	`Britain (PROPN) --[dobj]--> facing (VERB)`
6936	`at (ADP) --[prep]--> facing (VERB)`
6937	`this (DET) --[det]--> time (NOUN)`
6938	`time (NOUN) --[pobj]--> at (ADP)`
6939	`- (PUNCT) --[punct]--> 's (AUX)`
6940	`it (PRON) --[nsubj]--> 's (AUX)`
6941	`'s (AUX) --[ROOT]--> 's (AUX)`
6942	`quite (ADV) --[advmod]--> shocking (ADJ)`
6943	`shocking (ADJ) --[acomp]--> 's (AUX)`
6944	`that (SCONJ) --[mark]--> wanting (VERB)`
6945	`the (DET) --[det]--> minister (NOUN)`
6946	`prime (ADJ) --[amod]--> minister (NOUN)`
6947	`minister (NOUN) --[nsubj]--> wanting (VERB)`
6948	`is (AUX) --[aux]--> wanting (VERB)`
6949	`not (PART) --[neg]--> wanting (VERB)`
6950	`wanting (VERB) --[ccomp]--> shocking (ADJ)`
6951	`to (PART) --[aux]--> held (VERB)`
6952	`be (AUX) --[auxpass]--> held (VERB)`
6953	`held (VERB) --[xcomp]--> wanting (VERB)`
6954	`to (ADP) --[prep]--> held (VERB)`
6955	`account (NOUN) --[pobj]--> to (ADP)`
6956	`and (CCONJ) --[cc]--> held (VERB)`
6957	`seek (VERB) --[conj]--> held (VERB)`
6958	`the (DET) --[det]--> counsel (NOUN)`
6959	`counsel (NOUN) --[dobj]--> seek (VERB)`
6960	`of (ADP) --[prep]--> counsel (NOUN)`
6961	`his (PRON) --[poss]--> peers (NOUN)`
6962	`peers (NOUN) --[pobj]--> of (ADP)`
6963	`. (PUNCT) --[punct]--> 's (AUX)`
6964
6965	`Total sentences: 5`
6966
6967	`RAKE Keyphrase Relationships:`
6968	`noun phrase: "account" contains [account], [account]`
6969	`verb phrase: "tested 's never been" contains [tested], [never]`
6970	`verb phrase: "account to" contains [account], [account]`
6971	`verb phrase: "scrutinize to actions" contains [scrutinize], [actions]`
6972	`verb phrase: "preventing us on" contains [preventing us], [us]`
6973	`verb phrase: "seek counsel" contains [seek], [counsel]`
6974	`Total relationships found: 6`
6975
6976	`YAKE Keyphrase Relationships:`
6977	`noun phrase: "British democracy" contains [british democracy], [british]`
6978	`Total relationships found: 1`
6979
6980	`Timing Comparison:`
6981	`RAKE: 0.0000sec`
6982	`YAKE: 0.0239sec`
6983	`KeyBERT: 0.0772sec`
6984	`Dependencies: 0.0133sec`
6985	`Fastest: RAKE`
6986
6987	`================================================================================`
6988	`Message 53:`
6989	`GURNEY: People with Fragile X with an IQ of 40 are typically living with their parents or in an institutional setting. With an IQ of 50, in some cases, they're able to ride the bus. They're able to hold a job with some assistance, and they're able to function better in their community.`
6990	`--------------------------------------------------------------------------------`
6991	`RAKE Keywords:`
6992	`- typically living (score: 4.00) → typically live`
6993	`- institutional setting (score: 4.00)`
6994	`- function better (score: 4.00) → function well`
6995	`- fragile x (score: 4.00) → Fragile X`
6996	`- ride (score: 1.00)`
6997	`- people (score: 1.00) → People`
6998	`- parents (score: 1.00) → parent`
6999	`- job (score: 1.00)`
7000	`- iq (score: 1.00)`
7001	`- iq (score: 1.00)`
7002	`- hold (score: 1.00)`
7003	`- gurney (score: 1.00)`
7004	`- community (score: 1.00)`
7005	`- cases (score: 1.00) → case`
7006	`- bus (score: 1.00)`
7007	`- assistance (score: 1.00)`
7008	`- able (score: 1.00)`
7009	`- able (score: 1.00)`
7010	`- able (score: 1.00)`
7011	`- 50 (score: 1.00)`
7012	`- 40 (score: 1.00)`
7013	`Total keywords: 21 extracted in 0.0000 seconds`
7014
7015	`YAKE Keywords:`
7016	`- People with Fragile (score: 0.00) → People with Fragile`
7017	`- institutional setting (score: 0.01)`
7018	`- typically living (score: 0.01) → typically live`
7019	`- GURNEY (score: 0.04)`
7020	`- People (score: 0.05) → People`
7021	`- Fragile (score: 0.06) → Fragile`
7022	`- setting (score: 0.09)`
7023	`- ride the bus (score: 0.09)`
7024	`- typically (score: 0.12)`
7025	`- living (score: 0.12) → live`
7026	`- parents (score: 0.12) → parent`
7027	`- institutional (score: 0.12)`
7028	`- hold a job (score: 0.20)`
7029	`- cases (score: 0.26) → case`
7030	`- bus (score: 0.26)`
7031	`- ride (score: 0.32)`
7032	`- assistance (score: 0.34)`
7033	`- community (score: 0.34)`
7034	`- hold (score: 0.41)`
7035	`- job (score: 0.41)`
7036	`Total keywords: 20 extracted in 0.0080 seconds`
7037
7038	`KeyBERT Keywords:`
7039	`- fragile iq 40 (score: 0.66)`
7040	`- people fragile iq (score: 0.63)`
7041	`- fragile iq (score: 0.63)`
7042	`- iq 40 typically (score: 0.55)`
7043	`- gurney people fragile (score: 0.54)`
7044	`- iq 40 (score: 0.51)`
7045	`- iq 50 cases (score: 0.48)`
7046	`- people fragile (score: 0.47)`
7047	`- iq (score: 0.46)`
7048	`- iq 50 (score: 0.46)`
7049	`- setting iq 50 (score: 0.44)`
7050	`- setting iq (score: 0.42)`
7051	`- fragile (score: 0.42)`
7052	`- institutional setting iq (score: 0.41)`
7053	`- 40 typically living (score: 0.36)`
7054	`- bus able hold (score: 0.33)`
7055	`- ride bus able (score: 0.31)`
7056	`- bus able (score: 0.31)`
7057	`- able ride bus (score: 0.30)`
7058	`- gurney people (score: 0.29)`
7059	`Total keywords: 20 extracted in 0.0473 seconds`
7060
7061	`Dependency Relations (extracted in 0.0138sec):`
7062
7063	`Sentence 1: GURNEY: People with Fragile X with an IQ of 40 are typically living with their parents or in an institutional setting.`
7064	`GURNEY (NOUN) --[ROOT]--> GURNEY (NOUN)`
7065	`: (PUNCT) --[punct]--> GURNEY (NOUN)`
7066	`People (NOUN) --[nsubj]--> living (VERB)`
7067	`with (ADP) --[prep]--> People (NOUN)`
7068	`Fragile (PROPN) --[compound]--> X (PROPN)`
7069	`X (PROPN) --[pobj]--> with (ADP)`
7070	`with (ADP) --[prep]--> People (NOUN)`
7071	`an (DET) --[det]--> IQ (NOUN)`
7072	`IQ (NOUN) --[pobj]--> with (ADP)`
7073	`of (ADP) --[prep]--> IQ (NOUN)`
7074	`40 (NUM) --[pobj]--> of (ADP)`
7075	`are (AUX) --[aux]--> living (VERB)`
7076	`typically (ADV) --[advmod]--> living (VERB)`
7077	`living (VERB) --[acl]--> GURNEY (NOUN)`
7078	`with (ADP) --[prep]--> living (VERB)`
7079	`their (PRON) --[poss]--> parents (NOUN)`
7080	`parents (NOUN) --[pobj]--> with (ADP)`
7081	`or (CCONJ) --[cc]--> with (ADP)`
7082	`in (ADP) --[conj]--> with (ADP)`
7083	`an (DET) --[det]--> setting (NOUN)`
7084	`institutional (ADJ) --[amod]--> setting (NOUN)`
7085	`setting (NOUN) --[pobj]--> in (ADP)`
7086	`. (PUNCT) --[punct]--> living (VERB)`
7087
7088	`Sentence 2: With an IQ of 50, in some cases, they're able to ride the bus.`
7089	`With (ADP) --[prep]--> 're (AUX)`
7090	`an (DET) --[det]--> IQ (NOUN)`
7091	`IQ (NOUN) --[pobj]--> With (ADP)`
7092	`of (ADP) --[prep]--> IQ (NOUN)`
7093	`50 (NUM) --[pobj]--> of (ADP)`
7094	`, (PUNCT) --[punct]--> 're (AUX)`
7095	`in (ADP) --[prep]--> 're (AUX)`
7096	`some (DET) --[det]--> cases (NOUN)`
7097	`cases (NOUN) --[pobj]--> in (ADP)`
7098	`, (PUNCT) --[punct]--> 're (AUX)`
7099	`they (PRON) --[nsubj]--> 're (AUX)`
7100	`'re (AUX) --[ROOT]--> 're (AUX)`
7101	`able (ADJ) --[acomp]--> 're (AUX)`
7102	`to (PART) --[aux]--> ride (VERB)`
7103	`ride (VERB) --[xcomp]--> able (ADJ)`
7104	`the (DET) --[det]--> bus (NOUN)`
7105	`bus (NOUN) --[dobj]--> ride (VERB)`
7106	`. (PUNCT) --[punct]--> 're (AUX)`
7107
7108	`Sentence 3: They're able to hold a job with some assistance, and they're able to function better in their community.`
7109	`They (PRON) --[nsubj]--> 're (AUX)`
7110	`'re (AUX) --[ROOT]--> 're (AUX)`
7111	`able (ADJ) --[acomp]--> 're (AUX)`
7112	`to (PART) --[aux]--> hold (VERB)`
7113	`hold (VERB) --[xcomp]--> able (ADJ)`
7114	`a (DET) --[det]--> job (NOUN)`
7115	`job (NOUN) --[dobj]--> hold (VERB)`
7116	`with (ADP) --[prep]--> hold (VERB)`
7117	`some (DET) --[det]--> assistance (NOUN)`
7118	`assistance (NOUN) --[pobj]--> with (ADP)`
7119	`, (PUNCT) --[punct]--> 're (AUX)`
7120	`and (CCONJ) --[cc]--> 're (AUX)`
7121	`they (PRON) --[nsubj]--> 're (AUX)`
7122	`'re (AUX) --[conj]--> 're (AUX)`
7123	`able (ADJ) --[acomp]--> 're (AUX)`
7124	`to (PART) --[aux]--> function (VERB)`
7125	`function (VERB) --[xcomp]--> able (ADJ)`
7126	`better (ADV) --[advmod]--> function (VERB)`
7127	`in (ADP) --[prep]--> function (VERB)`
7128	`their (PRON) --[poss]--> community (NOUN)`
7129	`community (NOUN) --[pobj]--> in (ADP)`
7130	`. (PUNCT) --[punct]--> 're (AUX)`
7131
7132	`Total sentences: 3`
7133
7134	`RAKE Keyphrase Relationships:`
7135	`noun phrase: "an IQ" contains [iq], [iq]`
7136	`noun phrase: "an IQ" contains [iq], [iq]`
7137	`verb phrase: "ride to bus" contains [ride], [bus]`
7138	`verb phrase: "hold to job with" contains [job], [hold]`
7139	`Total relationships found: 4`
7140
7141	`YAKE Keyphrase Relationships:`
7142	`noun phrase: "an institutional setting" contains [institutional setting], [setting], [institutional]`
7143	`verb phrase: "living are typically with" contains [typically], [living]`
7144	`verb phrase: "ride to bus" contains [bus], [ride]`
7145	`verb phrase: "hold to job with" contains [hold], [job]`
7146	`Total relationships found: 4`
7147
7148	`Timing Comparison:`
7149	`RAKE: 0.0000sec`
7150	`YAKE: 0.0080sec`
7151	`KeyBERT: 0.0473sec`
7152	`Dependencies: 0.0138sec`
7153	`Fastest: RAKE`
7154
7155	`================================================================================`
7156	`Message 54:`
7157	`YU: So in his novel, Hamill explores the horrors of depression without judging Eunice. He says even though the creature stalks the Turner family throughout the years, it isn't the cause of Eunice's mental illness, and it isn't a metaphor for it, either.`
7158	`--------------------------------------------------------------------------------`
7159	`RAKE Keywords:`
7160	`- turner family throughout (score: 9.00) → Turner family throughout`
7161	`- says even though (score: 9.00) → say even though`
7162	`- mental illness (score: 4.00)`
7163	`- hamill explores (score: 4.00) → Hamill explore`
7164	`- creature stalks (score: 4.00) → creature stalk`
7165	`- yu (score: 1.00) → YU`
7166	`- years (score: 1.00) → year`
7167	`- novel (score: 1.00)`
7168	`- metaphor (score: 1.00)`
7169	`- horrors (score: 1.00) → horror`
7170	`- eunice (score: 1.00) → Eunice`
7171	`- either (score: 1.00)`
7172	`- cause (score: 1.00)`
7173	`Total keywords: 13 extracted in 0.0000 seconds`
7174
7175	`YAKE Keywords:`
7176	`- Hamill explores (score: 0.01) → Hamill explore`
7177	`- Eunice mental illness (score: 0.01)`
7178	`- explores the horrors (score: 0.02) → explore the horror`
7179	`- horrors of depression (score: 0.02) → horror of depression`
7180	`- depression without judging (score: 0.02) → depression without judge`
7181	`- judging Eunice (score: 0.02) → judge Eunice`
7182	`- Eunice mental (score: 0.05)`
7183	`- Hamill (score: 0.05) → Hamill`
7184	`- Eunice (score: 0.07) → Eunice`
7185	`- stalks the Turner (score: 0.07) → stalk the Turner`
7186	`- Turner family (score: 0.07) → Turner family`
7187	`- mental illness (score: 0.10)`
7188	`- explores (score: 0.13) → explore`
7189	`- horrors (score: 0.13) → horror`
7190	`- depression (score: 0.13)`
7191	`- judging (score: 0.13) → judge`
7192	`- creature stalks (score: 0.13) → creature stalk`
7193	`- Turner (score: 0.19) → Turner`
7194	`- years (score: 0.27) → year`
7195	`- illness (score: 0.27)`
7196	`Total keywords: 20 extracted in 0.0215 seconds`
7197
7198	`KeyBERT Keywords:`
7199	`- depression judging eunice (score: 0.68)`
7200	`- hamill explores horrors (score: 0.62)`
7201	`- eunice mental illness (score: 0.62)`
7202	`- eunice mental (score: 0.61)`
7203	`- cause eunice mental (score: 0.59)`
7204	`- isn cause eunice (score: 0.56)`
7205	`- judging eunice says (score: 0.55)`
7206	`- novel hamill explores (score: 0.54)`
7207	`- novel hamill (score: 0.53)`
7208	`- judging eunice (score: 0.52)`
7209	`- cause eunice (score: 0.51)`
7210	`- eunice says (score: 0.50)`
7211	`- eunice says creature (score: 0.50)`
7212	`- yu novel hamill (score: 0.49)`
7213	`- eunice (score: 0.48)`
7214	`- hamill explores (score: 0.47)`
7215	`- explores horrors depression (score: 0.45)`
7216	`- horrors depression judging (score: 0.43)`
7217	`- hamill (score: 0.43)`
7218	`- mental illness (score: 0.40)`
7219	`Total keywords: 20 extracted in 0.0421 seconds`
7220
7221	`Dependency Relations (extracted in 0.0097sec):`
7222
7223	`Sentence 1: YU:`
7224	`YU (PROPN) --[ROOT]--> YU (PROPN)`
7225	`: (PUNCT) --[punct]--> YU (PROPN)`
7226
7227	`Sentence 2: So in his novel, Hamill explores the horrors of depression without judging Eunice.`
7228	`So (ADV) --[advmod]--> explores (VERB)`
7229	`in (ADP) --[prep]--> explores (VERB)`
7230	`his (PRON) --[poss]--> novel (NOUN)`
7231	`novel (NOUN) --[pobj]--> in (ADP)`
7232	`, (PUNCT) --[punct]--> explores (VERB)`
7233	`Hamill (PROPN) --[nsubj]--> explores (VERB)`
7234	`explores (VERB) --[ROOT]--> explores (VERB)`
7235	`the (DET) --[det]--> horrors (NOUN)`
7236	`horrors (NOUN) --[dobj]--> explores (VERB)`
7237	`of (ADP) --[prep]--> horrors (NOUN)`
7238	`depression (NOUN) --[pobj]--> of (ADP)`
7239	`without (ADP) --[prep]--> explores (VERB)`
7240	`judging (VERB) --[pcomp]--> without (ADP)`
7241	`Eunice (PROPN) --[dobj]--> judging (VERB)`
7242	`. (PUNCT) --[punct]--> explores (VERB)`
7243
7244	`Sentence 3: He says even though the creature stalks the Turner family throughout the years, it isn't the cause of Eunice's mental illness, and it isn't a metaphor for it, either.`
7245	`He (PRON) --[nsubj]--> says (VERB)`
7246	`says (VERB) --[ROOT]--> says (VERB)`
7247	`even (ADV) --[advmod]--> stalks (VERB)`
7248	`though (SCONJ) --[mark]--> stalks (VERB)`
7249	`the (DET) --[det]--> creature (NOUN)`
7250	`creature (NOUN) --[nsubj]--> stalks (VERB)`
7251	`stalks (VERB) --[advcl]--> is (AUX)`
7252	`the (DET) --[det]--> family (NOUN)`
7253	`Turner (PROPN) --[compound]--> family (NOUN)`
7254	`family (NOUN) --[dobj]--> stalks (VERB)`
7255	`throughout (ADP) --[prep]--> stalks (VERB)`
7256	`the (DET) --[det]--> years (NOUN)`
7257	`years (NOUN) --[pobj]--> throughout (ADP)`
7258	`, (PUNCT) --[punct]--> is (AUX)`
7259	`it (PRON) --[nsubj]--> is (AUX)`
7260	`is (AUX) --[ccomp]--> says (VERB)`
7261	`n't (PART) --[neg]--> is (AUX)`
7262	`the (DET) --[det]--> cause (NOUN)`
7263	`cause (NOUN) --[attr]--> is (AUX)`
7264	`of (ADP) --[prep]--> cause (NOUN)`
7265	`Eunice (PROPN) --[poss]--> illness (NOUN)`
7266	`'s (PART) --[case]--> Eunice (PROPN)`
7267	`mental (ADJ) --[amod]--> illness (NOUN)`
7268	`illness (NOUN) --[pobj]--> of (ADP)`
7269	`, (PUNCT) --[punct]--> is (AUX)`
7270	`and (CCONJ) --[cc]--> is (AUX)`
7271	`it (PRON) --[nsubj]--> is (AUX)`
7272	`is (AUX) --[conj]--> is (AUX)`
7273	`n't (PART) --[neg]--> is (AUX)`
7274	`a (DET) --[det]--> metaphor (NOUN)`
7275	`metaphor (NOUN) --[attr]--> is (AUX)`
7276	`for (ADP) --[prep]--> metaphor (NOUN)`
7277	`it (PRON) --[pobj]--> for (ADP)`
7278	`, (PUNCT) --[punct]--> is (AUX)`
7279	`either (ADV) --[advmod]--> is (AUX)`
7280	`. (PUNCT) --[punct]--> says (VERB)`
7281
7282	`Total sentences: 3`
7283
7284	`RAKE Keyphrase Relationships:`
7285	`noun phrase: "Eunice's mental illness" contains [mental illness], [eunice]`
7286	`Total relationships found: 1`
7287
7288	`YAKE Keyphrase Relationships:`
7289	`noun phrase: "the Turner family" contains [turner family], [turner]`
7290	`noun phrase: "Eunice's mental illness" contains [eunice], [mental illness], [illness]`
7291	`verb phrase: "explores So in horrors without" contains [explores], [horrors]`
7292	`verb phrase: "judging Eunice" contains [judging eunice], [eunice], [judging]`
7293	`Total relationships found: 4`
7294
7295	`Timing Comparison:`
7296	`RAKE: 0.0000sec`
7297	`YAKE: 0.0215sec`
7298	`KeyBERT: 0.0421sec`
7299	`Dependencies: 0.0097sec`
7300	`Fastest: RAKE`
7301
7302	`================================================================================`
7303	`Message 55:`
7304	`BLAIR: There's no remote in a movie theater. But even when you're at home, all it takes is a few seconds of gore to scare a child. That's what happened to Kaari Pitkin recently when she was watching the comedy "Brooklyn Nine-Nine" with her 11-year-old daughter.(SOUNDBITE OF TV SHOW, "BROOKLYN NINE-NINE")`
7305	`--------------------------------------------------------------------------------`
7306	`RAKE Keywords:`
7307	`- kaari pitkin recently (score: 9.00) → Kaari Pitkin recently`
7308	`- tv show (score: 4.00) → TV SHOW`
7309	`- old daughter (score: 4.00)`
7310	`- movie theater (score: 4.00)`
7311	`- nine ") (score: 3.75)`
7312	`- brooklyn nine (score: 3.75) → Brooklyn Nine`
7313	`- brooklyn nine (score: 3.75) → Brooklyn Nine`
7314	`- nine (score: 1.75) → Nine`
7315	`- year (score: 1.00)`
7316	`- watching (score: 1.00) → watch`
7317	`- takes (score: 1.00) → take`
7318	`- soundbite (score: 1.00)`
7319	`- seconds (score: 1.00) → second`
7320	`- scare (score: 1.00)`
7321	`- remote (score: 1.00)`
7322	`- home (score: 1.00)`
7323	`- happened (score: 1.00) → happen`
7324	`- gore (score: 1.00)`
7325	`- even (score: 1.00)`
7326	`- comedy (score: 1.00)`
7327	`- child (score: 1.00)`
7328	`- blair (score: 1.00) → BLAIR`
7329	`- 11 (score: 1.00)`
7330	`Total keywords: 23 extracted in 0.0000 seconds`
7331
7332	`YAKE Keywords:`
7333	`- movie theater (score: 0.03)`
7334	`- Brooklyn Nine-Nine (score: 0.03)`
7335	`- BLAIR (score: 0.04) → BLAIR`
7336	`- Kaari Pitkin recently (score: 0.09) → Kaari Pitkin recently`
7337	`- Kaari Pitkin (score: 0.12) → Kaari Pitkin`
7338	`- Brooklyn (score: 0.12) → Brooklyn`
7339	`- Nine-Nine (score: 0.12)`
7340	`- theater (score: 0.14)`
7341	`- scare a child (score: 0.20)`
7342	`- remote (score: 0.20)`
7343	`- movie (score: 0.20)`
7344	`- happened to Kaari (score: 0.22) → happen to Kaari`
7345	`- Pitkin recently (score: 0.22) → Pitkin recently`
7346	`- SOUNDBITE (score: 0.26)`
7347	`- SHOW (score: 0.26) → SHOW`
7348	`- gore to scare (score: 0.27)`
7349	`- daughter. (score: 0.31)`
7350	`- Kaari (score: 0.33) → Kaari`
7351	`- Pitkin (score: 0.33) → Pitkin`
7352	`- watching the comedy (score: 0.33) → watch the comedy`
7353	`Total keywords: 20 extracted in 0.0172 seconds`
7354
7355	`KeyBERT Keywords:`
7356	`- blair remote movie (score: 0.59)`
7357	`- remote movie (score: 0.56)`
7358	`- remote movie theater (score: 0.52)`
7359	`- blair remote (score: 0.49)`
7360	`- gore scare child (score: 0.46)`
7361	`- scare child happened (score: 0.44)`
7362	`- scare child (score: 0.40)`
7363	`- movie theater home (score: 0.39)`
7364	`- daughter soundbite tv (score: 0.38)`
7365	`- movie theater (score: 0.38)`
7366	`- child happened kaari (score: 0.36)`
7367	`- daughter soundbite (score: 0.35)`
7368	`- remote (score: 0.35)`
7369	`- child happened (score: 0.34)`
7370	`- old daughter soundbite (score: 0.34)`
7371	`- theater home (score: 0.34)`
7372	`- blair (score: 0.34)`
7373	`- pitkin recently watching (score: 0.33)`
7374	`- theater home takes (score: 0.33)`
7375	`- happened kaari pitkin (score: 0.32)`
7376	`Total keywords: 20 extracted in 0.0533 seconds`
7377
7378	`Dependency Relations (extracted in 0.0040sec):`
7379
7380	`Sentence 1: BLAIR: There's no remote in a movie theater.`
7381	`BLAIR (PROPN) --[ROOT]--> BLAIR (PROPN)`
7382	`: (PUNCT) --[punct]--> BLAIR (PROPN)`
7383	`There (PRON) --[expl]--> 's (VERB)`
7384	`'s (VERB) --[acl]--> BLAIR (PROPN)`
7385	`no (DET) --[det]--> remote (NOUN)`
7386	`remote (NOUN) --[attr]--> 's (VERB)`
7387	`in (ADP) --[prep]--> remote (NOUN)`
7388	`a (DET) --[det]--> theater (NOUN)`
7389	`movie (NOUN) --[compound]--> theater (NOUN)`
7390	`theater (NOUN) --[pobj]--> in (ADP)`
7391	`. (PUNCT) --[punct]--> BLAIR (PROPN)`
7392
7393	`Sentence 2: But even when you're at home, all it takes is a few seconds of gore to scare a child.`
7394	`But (CCONJ) --[cc]--> is (AUX)`
7395	`even (ADV) --[advmod]--> 're (AUX)`
7396	`when (SCONJ) --[advmod]--> 're (AUX)`
7397	`you (PRON) --[nsubj]--> 're (AUX)`
7398	`'re (AUX) --[advcl]--> is (AUX)`
7399	`at (ADP) --[prep]--> 're (AUX)`
7400	`home (NOUN) --[pobj]--> at (ADP)`
7401	`, (PUNCT) --[punct]--> is (AUX)`
7402	`all (PRON) --[nsubj]--> is (AUX)`
7403	`it (PRON) --[nsubj]--> takes (VERB)`
7404	`takes (VERB) --[relcl]--> all (PRON)`
7405	`is (AUX) --[ROOT]--> is (AUX)`
7406	`a (DET) --[quantmod]--> few (ADJ)`
7407	`few (ADJ) --[nummod]--> seconds (NOUN)`
7408	`seconds (NOUN) --[attr]--> is (AUX)`
7409	`of (ADP) --[prep]--> seconds (NOUN)`
7410	`gore (PROPN) --[pobj]--> of (ADP)`
7411	`to (PART) --[aux]--> scare (VERB)`
7412	`scare (VERB) --[xcomp]--> is (AUX)`
7413	`a (DET) --[det]--> child (NOUN)`
7414	`child (NOUN) --[dobj]--> scare (VERB)`
7415	`. (PUNCT) --[punct]--> is (AUX)`
7416
7417	`Sentence 3: That's what happened to Kaari Pitkin recently when she was watching the comedy "Brooklyn Nine-Nine" with her 11-year-old daughter.(SOUNDBITE OF TV SHOW, "BROOKLYN NINE-NINE")`
7418	`That (PRON) --[nsubj]--> 's (AUX)`
7419	`'s (AUX) --[ROOT]--> 's (AUX)`
7420	`what (PRON) --[nsubj]--> happened (VERB)`
7421	`happened (VERB) --[ccomp]--> 's (AUX)`
7422	`to (ADP) --[prep]--> happened (VERB)`
7423	`Kaari (PROPN) --[compound]--> Pitkin (PROPN)`
7424	`Pitkin (PROPN) --[pobj]--> to (ADP)`
7425	`recently (ADV) --[advmod]--> happened (VERB)`
7426	`when (SCONJ) --[advmod]--> watching (VERB)`
7427	`she (PRON) --[nsubj]--> watching (VERB)`
7428	`was (AUX) --[aux]--> watching (VERB)`
7429	`watching (VERB) --[advcl]--> happened (VERB)`
7430	`the (DET) --[det]--> comedy (NOUN)`
7431	`comedy (NOUN) --[dobj]--> watching (VERB)`
7432	`" (PUNCT) --[punct]--> comedy (NOUN)`
7433	`Brooklyn (PROPN) --[compound]--> Nine (PROPN)`
7434	`Nine (PROPN) --[compound]--> Nine (PROPN)`
7435	`- (PUNCT) --[punct]--> Nine (PROPN)`
7436	`Nine (PROPN) --[appos]--> comedy (NOUN)`
7437	`" (PUNCT) --[punct]--> comedy (NOUN)`
7438	`with (ADP) --[prep]--> watching (VERB)`
7439	`her (PRON) --[poss]--> daughter.(SOUNDBITE (NOUN)`
7440	`11 (NUM) --[nummod]--> year (NOUN)`
7441	`- (PUNCT) --[punct]--> year (NOUN)`
7442	`year (NOUN) --[npadvmod]--> old (ADJ)`
7443	`- (PUNCT) --[punct]--> old (ADJ)`
7444	`old (ADJ) --[amod]--> daughter.(SOUNDBITE (NOUN)`
7445	`daughter.(SOUNDBITE (NOUN) --[pobj]--> with (ADP)`
7446	`OF (ADP) --[prep]--> daughter.(SOUNDBITE (NOUN)`
7447	`TV (PROPN) --[compound]--> SHOW (PROPN)`
7448	`SHOW (PROPN) --[pobj]--> OF (ADP)`
7449	`, (PUNCT) --[punct]--> 's (AUX)`
7450	`" (PUNCT) --[punct]--> 's (AUX)`
7451	`BROOKLYN (PROPN) --[dep]--> 's (AUX)`
7452	`NINE (PROPN) --[nummod]--> BROOKLYN (PROPN)`
7453	`- (PUNCT) --[punct]--> NINE (NUM)`
7454	`NINE (NUM) --[prep]--> NINE (PROPN)`
7455	`" (PUNCT) --[punct]--> 's (AUX)`
7456	`) (PUNCT) --[punct]--> 's (AUX)`
7457
7458	`Total sentences: 3`
7459
7460	`RAKE Keyphrase Relationships:`
7461	`noun phrase: "Brooklyn Nine-Nine" contains [brooklyn nine], [brooklyn nine], [nine]`
7462	`noun phrase: "her 11-year-old daughter.(SOUNDBITE" contains [old daughter], [year], [soundbite], [11]`
7463	`verb phrase: "scare to child" contains [scare], [child]`
7464	`verb phrase: "watching when was comedy with" contains [watching], [comedy]`
7465	`Total relationships found: 4`
7466
7467	`YAKE Keyphrase Relationships:`
7468	`noun phrase: "a movie theater" contains [movie theater], [theater], [movie]`
7469	`noun phrase: "Kaari Pitkin" contains [kaari pitkin], [kaari], [pitkin]`
7470	`noun phrase: "Brooklyn Nine-Nine" contains [brooklyn nine-nine], [brooklyn], [nine-nine]`
7471	`noun phrase: "her 11-year-old daughter.(SOUNDBITE" contains [soundbite], [daughter.]`
7472	`Total relationships found: 4`
7473
7474	`Timing Comparison:`
7475	`RAKE: 0.0000sec`
7476	`YAKE: 0.0172sec`
7477	`KeyBERT: 0.0533sec`
7478	`Dependencies: 0.0040sec`
7479	`Fastest: RAKE`
7480
7481	`================================================================================`
7482	`Message 56:`
7483	`AILSA CHANG: When Amazon CEO Jeff Bezos testified before Congress this week, he was asked to answer these questions - does the company use its power to squash competition, and what does that mean for consumers? Well, one group that was largely left out of that conversation were the workers. NPR's Brianna Scott spoke to a few Amazon warehouse employees about that.`
7484	`--------------------------------------------------------------------------------`
7485	`RAKE Keywords:`
7486	`- brianna scott spoke (score: 9.00) → Brianna Scott speak`
7487	`- amazon warehouse employees (score: 9.00) → Amazon warehouse employee`
7488	`- squash competition (score: 4.00)`
7489	`- one group (score: 4.00)`
7490	`- largely left (score: 4.00) → largely leave`
7491	`- company use (score: 4.00)`
7492	`- ailsa chang (score: 4.00) → AILSA CHANG`
7493	`- workers (score: 1.00) → worker`
7494	`- well (score: 1.00)`
7495	`- week (score: 1.00)`
7496	`- questions (score: 1.00) → question`
7497	`- power (score: 1.00)`
7498	`- npr (score: 1.00) → NPR`
7499	`- mean (score: 1.00)`
7500	`- conversation (score: 1.00)`
7501	`- consumers (score: 1.00) → consumer`
7502	`- congress (score: 1.00) → Congress`
7503	`- asked (score: 1.00) → ask`
7504	`- answer (score: 1.00)`
7505	`Total keywords: 19 extracted in 0.0000 seconds`
7506
7507	`YAKE Keywords:`
7508	`- CEO Jeff Bezos (score: 0.00) → CEO Jeff Bezos`
7509	`- Jeff Bezos testified (score: 0.00) → Jeff Bezos testify`
7510	`- Amazon CEO Jeff (score: 0.00) → Amazon CEO Jeff`
7511	`- AILSA CHANG (score: 0.00) → AILSA CHANG`
7512	`- CEO Jeff (score: 0.01) → CEO Jeff`
7513	`- Jeff Bezos (score: 0.01) → Jeff Bezos`
7514	`- Congress this week (score: 0.01) → Congress this week`
7515	`- Bezos testified (score: 0.01) → Bezos testify`
7516	`- testified before Congress (score: 0.01) → testify before Congress`
7517	`- Amazon CEO (score: 0.02) → Amazon CEO`
7518	`- answer these questions (score: 0.02) → answer these question`
7519	`- squash competition (score: 0.02)`
7520	`- NPR Brianna Scott (score: 0.02)`
7521	`- asked to answer (score: 0.02) → ask to answer`
7522	`- power to squash (score: 0.02)`
7523	`- Brianna Scott spoke (score: 0.06) → Brianna Scott speak`
7524	`- AILSA (score: 0.06) → AILSA`
7525	`- CHANG (score: 0.06) → CHANG`
7526	`- NPR Brianna (score: 0.07)`
7527	`- CEO (score: 0.07) → CEO`
7528	`Total keywords: 20 extracted in 0.0294 seconds`
7529
7530	`KeyBERT Keywords:`
7531	`- chang amazon ceo (score: 0.58)`
7532	`- amazon ceo (score: 0.58)`
7533	`- amazon ceo jeff (score: 0.56)`
7534	`- ceo jeff bezos (score: 0.55)`
7535	`- jeff bezos (score: 0.55)`
7536	`- bezos (score: 0.53)`
7537	`- bezos testified congress (score: 0.52)`
7538	`- amazon warehouse employees (score: 0.51)`
7539	`- jeff bezos testified (score: 0.50)`
7540	`- bezos testified (score: 0.48)`
7541	`- questions does company (score: 0.45)`
7542	`- spoke amazon warehouse (score: 0.42)`
7543	`- ceo jeff (score: 0.41)`
7544	`- consumers (score: 0.40)`
7545	`- ailsa chang amazon (score: 0.40)`
7546	`- amazon warehouse (score: 0.40)`
7547	`- scott spoke amazon (score: 0.39)`
7548	`- chang amazon (score: 0.39)`
7549	`- ceo (score: 0.39)`
7550	`- warehouse employees (score: 0.38)`
7551	`Total keywords: 20 extracted in 0.0496 seconds`
7552
7553	`Dependency Relations (extracted in 0.0077sec):`
7554
7555	`Sentence 1: AILSA CHANG: When Amazon CEO Jeff Bezos testified before Congress this week, he was asked to answer these questions - does the company use its power to squash competition, and what does that mean for consumers?`
7556	`AILSA (PROPN) --[compound]--> CHANG (PROPN)`
7557	`CHANG (PROPN) --[nsubjpass]--> asked (VERB)`
7558	`: (PUNCT) --[punct]--> CHANG (PROPN)`
7559	`When (SCONJ) --[advmod]--> testified (VERB)`
7560	`Amazon (PROPN) --[compound]--> CEO (PROPN)`
7561	`CEO (PROPN) --[compound]--> Bezos (PROPN)`
7562	`Jeff (PROPN) --[compound]--> Bezos (PROPN)`
7563	`Bezos (PROPN) --[nsubj]--> testified (VERB)`
7564	`testified (VERB) --[advcl]--> asked (VERB)`
7565	`before (ADP) --[prep]--> testified (VERB)`
7566	`Congress (PROPN) --[pobj]--> before (ADP)`
7567	`this (DET) --[det]--> week (NOUN)`
7568	`week (NOUN) --[npadvmod]--> testified (VERB)`
7569	`, (PUNCT) --[punct]--> asked (VERB)`
7570	`he (PRON) --[nsubjpass]--> asked (VERB)`
7571	`was (AUX) --[auxpass]--> asked (VERB)`
7572	`asked (VERB) --[ccomp]--> use (VERB)`
7573	`to (PART) --[aux]--> answer (VERB)`
7574	`answer (VERB) --[xcomp]--> asked (VERB)`
7575	`these (DET) --[det]--> questions (NOUN)`
7576	`questions (NOUN) --[dobj]--> answer (VERB)`
7577	`- (PUNCT) --[punct]--> use (VERB)`
7578	`does (AUX) --[aux]--> use (VERB)`
7579	`the (DET) --[det]--> company (NOUN)`
7580	`company (NOUN) --[nsubj]--> use (VERB)`
7581	`use (VERB) --[ROOT]--> use (VERB)`
7582	`its (PRON) --[poss]--> power (NOUN)`
7583	`power (NOUN) --[dobj]--> use (VERB)`
7584	`to (PART) --[aux]--> squash (VERB)`
7585	`squash (VERB) --[acl]--> power (NOUN)`
7586	`competition (NOUN) --[dobj]--> squash (VERB)`
7587	`, (PUNCT) --[punct]--> use (VERB)`
7588	`and (CCONJ) --[cc]--> use (VERB)`
7589	`what (PRON) --[dobj]--> mean (VERB)`
7590	`does (AUX) --[aux]--> mean (VERB)`
7591	`that (PRON) --[nsubj]--> mean (VERB)`
7592	`mean (VERB) --[conj]--> use (VERB)`
7593	`for (ADP) --[prep]--> mean (VERB)`
7594	`consumers (NOUN) --[pobj]--> for (ADP)`
7595	`? (PUNCT) --[punct]--> use (VERB)`
7596
7597	`Sentence 2: Well, one group that was largely left out of that conversation were the workers.`
7598	`Well (INTJ) --[intj]--> were (AUX)`
7599	`, (PUNCT) --[punct]--> were (AUX)`
7600	`one (NUM) --[nummod]--> group (NOUN)`
7601	`group (NOUN) --[nsubj]--> were (AUX)`
7602	`that (PRON) --[nsubjpass]--> left (VERB)`
7603	`was (AUX) --[auxpass]--> left (VERB)`
7604	`largely (ADV) --[advmod]--> left (VERB)`
7605	`left (VERB) --[relcl]--> group (NOUN)`
7606	`out (ADP) --[prep]--> left (VERB)`
7607	`of (ADP) --[prep]--> out (ADP)`
7608	`that (DET) --[det]--> conversation (NOUN)`
7609	`conversation (NOUN) --[pobj]--> of (ADP)`
7610	`were (AUX) --[ROOT]--> were (AUX)`
7611	`the (DET) --[det]--> workers (NOUN)`
7612	`workers (NOUN) --[attr]--> were (AUX)`
7613	`. (PUNCT) --[punct]--> were (AUX)`
7614
7615	`Sentence 3: NPR's Brianna Scott spoke to a few Amazon warehouse employees about that.`
7616	`NPR (PROPN) --[poss]--> Scott (PROPN)`
7617	`'s (PART) --[case]--> NPR (PROPN)`
7618	`Brianna (PROPN) --[compound]--> Scott (PROPN)`
7619	`Scott (PROPN) --[nsubj]--> spoke (VERB)`
7620	`spoke (VERB) --[ROOT]--> spoke (VERB)`
7621	`to (ADP) --[prep]--> spoke (VERB)`
7622	`a (DET) --[quantmod]--> few (ADJ)`
7623	`few (ADJ) --[nummod]--> warehouse (NOUN)`
7624	`Amazon (PROPN) --[compound]--> warehouse (NOUN)`
7625	`warehouse (NOUN) --[compound]--> employees (NOUN)`
7626	`employees (NOUN) --[pobj]--> to (ADP)`
7627	`about (ADP) --[prep]--> spoke (VERB)`
7628	`that (PRON) --[pobj]--> about (ADP)`
7629	`. (PUNCT) --[punct]--> spoke (VERB)`
7630
7631	`Total sentences: 3`
7632
7633	`RAKE Keyphrase Relationships:`
7634	`verb phrase: "answer to questions" contains [questions], [answer]`
7635	`Total relationships found: 1`
7636
7637	`YAKE Keyphrase Relationships:`
7638	`noun phrase: "AILSA CHANG" contains [ailsa chang], [ailsa], [chang]`
7639	`noun phrase: "Amazon CEO Jeff Bezos" contains [ceo jeff bezos], [amazon ceo jeff], [ceo jeff], [jeff bezos], [amazon ceo], [ceo]`
7640	`Total relationships found: 2`
7641
7642	`Timing Comparison:`
7643	`RAKE: 0.0000sec`
7644	`YAKE: 0.0294sec`
7645	`KeyBERT: 0.0496sec`
7646	`Dependencies: 0.0077sec`
7647	`Fastest: RAKE`
7648
7649	`================================================================================`
7650	`Message 57:`
7651	`MONTANARO: ...That he wants to be able to show leadership for the United States - that the U.S. is doing something. But not being able to find agreement with his own party certainly does not make him look great.`
7652	`--------------------------------------------------------------------------------`
7653	`RAKE Keywords:`
7654	`- united states (score: 4.00) → United States`
7655	`- show leadership (score: 4.00)`
7656	`- party certainly (score: 4.00)`
7657	`- look great (score: 4.00)`
7658	`- find agreement (score: 4.00)`
7659	`- wants (score: 1.00) → want`
7660	`- u (score: 1.00)`
7661	`- something (score: 1.00)`
7662	`- montanaro (score: 1.00) → MONTANARO`
7663	`- make (score: 1.00)`
7664	`- able (score: 1.00)`
7665	`- able (score: 1.00)`
7666	`- ... (score: 1.00)`
7667	`Total keywords: 13 extracted in 0.0000 seconds`
7668
7669	`YAKE Keywords:`
7670	`- United States (score: 0.00) → United States`
7671	`- show leadership (score: 0.02)`
7672	`- MONTANARO (score: 0.04) → MONTANARO`
7673	`- States (score: 0.06) → States`
7674	`- United (score: 0.08) → United`
7675	`- show (score: 0.15)`
7676	`- leadership (score: 0.15)`
7677	`- find agreement (score: 0.17)`
7678	`- great (score: 0.28)`
7679	`- find (score: 0.38)`
7680	`- agreement (score: 0.38)`
7681	`- party (score: 0.38)`
7682	`- make (score: 0.38)`
7683	`Total keywords: 13 extracted in 0.0057 seconds`
7684
7685	`KeyBERT Keywords:`
7686	`- montanaro wants (score: 0.66)`
7687	`- montanaro wants able (score: 0.62)`
7688	`- montanaro (score: 0.60)`
7689	`- leadership united states (score: 0.43)`
7690	`- wants able leadership (score: 0.38)`
7691	`- agreement party certainly (score: 0.36)`
7692	`- able leadership united (score: 0.36)`
7693	`- able leadership (score: 0.35)`
7694	`- leadership (score: 0.35)`
7695	`- leadership united (score: 0.32)`
7696	`- agreement party (score: 0.31)`
7697	`- able agreement party (score: 0.31)`
7698	`- agreement (score: 0.29)`
7699	`- united states doing (score: 0.29)`
7700	`- able agreement (score: 0.28)`
7701	`- doing able agreement (score: 0.28)`
7702	`- party certainly (score: 0.22)`
7703	`- states doing able (score: 0.22)`
7704	`- states doing (score: 0.22)`
7705	`- party certainly does (score: 0.21)`
7706	`Total keywords: 20 extracted in 0.0338 seconds`
7707
7708	`Dependency Relations (extracted in 0.0140sec):`
7709
7710	`Sentence 1: MONTANARO: ...That he wants to be able to show leadership for the United States - that the U.S. is doing something.`
7711	`MONTANARO (PROPN) --[dep]--> wants (VERB)`
7712	`: (PUNCT) --[punct]--> MONTANARO (PROPN)`
7713	`... (PUNCT) --[punct]--> MONTANARO (PROPN)`
7714	`That (SCONJ) --[mark]--> wants (VERB)`
7715	`he (PRON) --[nsubj]--> wants (VERB)`
7716	`wants (VERB) --[ROOT]--> wants (VERB)`
7717	`to (PART) --[aux]--> be (AUX)`
7718	`be (AUX) --[xcomp]--> wants (VERB)`
7719	`able (ADJ) --[acomp]--> be (AUX)`
7720	`to (PART) --[aux]--> show (VERB)`
7721	`show (VERB) --[xcomp]--> able (ADJ)`
7722	`leadership (NOUN) --[dobj]--> show (VERB)`
7723	`for (ADP) --[prep]--> leadership (NOUN)`
7724	`the (DET) --[det]--> States (PROPN)`
7725	`United (PROPN) --[compound]--> States (PROPN)`
7726	`States (PROPN) --[pobj]--> for (ADP)`
7727	`- (PUNCT) --[punct]--> wants (VERB)`
7728	`that (SCONJ) --[mark]--> doing (VERB)`
7729	`the (DET) --[det]--> U.S. (PROPN)`
7730	`U.S. (PROPN) --[nsubj]--> doing (VERB)`
7731	`is (AUX) --[aux]--> doing (VERB)`
7732	`doing (VERB) --[ccomp]--> wants (VERB)`
7733	`something (PRON) --[dobj]--> doing (VERB)`
7734	`. (PUNCT) --[punct]--> wants (VERB)`
7735
7736	`Sentence 2: But not being able to find agreement with his own party certainly does not make him look great.`
7737	`But (CCONJ) --[cc]--> being (AUX)`
7738	`not (PART) --[neg]--> being (AUX)`
7739	`being (AUX) --[csubj]--> make (VERB)`
7740	`able (ADJ) --[acomp]--> being (AUX)`
7741	`to (PART) --[aux]--> find (VERB)`
7742	`find (VERB) --[xcomp]--> able (ADJ)`
7743	`agreement (NOUN) --[dobj]--> find (VERB)`
7744	`with (ADP) --[prep]--> agreement (NOUN)`
7745	`his (PRON) --[poss]--> party (NOUN)`
7746	`own (ADJ) --[amod]--> party (NOUN)`
7747	`party (NOUN) --[pobj]--> with (ADP)`
7748	`certainly (ADV) --[advmod]--> make (VERB)`
7749	`does (AUX) --[aux]--> make (VERB)`
7750	`not (PART) --[neg]--> make (VERB)`
7751	`make (VERB) --[ROOT]--> make (VERB)`
7752	`him (PRON) --[nsubj]--> look (VERB)`
7753	`look (VERB) --[ccomp]--> make (VERB)`
7754	`great (ADJ) --[acomp]--> look (VERB)`
7755	`. (PUNCT) --[punct]--> make (VERB)`
7756
7757	`Total sentences: 2`
7758
7759	`RAKE Keyphrase Relationships:`
7760	`noun phrase: "the United States" contains [united states], [u]`
7761	`Total relationships found: 1`
7762
7763	`YAKE Keyphrase Relationships:`
7764	`noun phrase: "the United States" contains [united states], [states], [united]`
7765	`verb phrase: "show to leadership" contains [show], [leadership]`
7766	`verb phrase: "find to agreement" contains [find], [agreement]`
7767	`Total relationships found: 3`
7768
7769	`Timing Comparison:`
7770	`RAKE: 0.0000sec`
7771	`YAKE: 0.0057sec`
7772	`KeyBERT: 0.0338sec`
7773	`Dependencies: 0.0140sec`
7774	`Fastest: RAKE`
7775
7776	`================================================================================`
7777	`Message 58:`
7778	`LETITIA JAMES: But let us not forget the masterminds, the family enterprise behind this crisis, the family that literally profited off of the suffering, the death of countless New Yorkers - the Sackler family, the owners of Purdue.`
7779	`--------------------------------------------------------------------------------`
7780	`RAKE Keywords:`
7781	`- countless new yorkers (score: 9.00) → countless New Yorkers`
7782	`- family enterprise behind (score: 8.00)`
7783	`- sackler family (score: 4.00) → Sackler family`
7784	`- literally profited (score: 4.00) → literally profit`
7785	`- letitia james (score: 4.00) → LETITIA JAMES`
7786	`- let us (score: 4.00) → let we`
7787	`- family (score: 2.00)`
7788	`- suffering (score: 1.00)`
7789	`- purdue (score: 1.00) → Purdue`
7790	`- owners (score: 1.00) → owner`
7791	`- masterminds (score: 1.00) → mastermind`
7792	`- forget (score: 1.00)`
7793	`- death (score: 1.00)`
7794	`- crisis (score: 1.00)`
7795	`Total keywords: 14 extracted in 0.0000 seconds`
7796
7797	`YAKE Keywords:`
7798	`- LETITIA JAMES (score: 0.00) → LETITIA JAMES`
7799	`- countless New Yorkers (score: 0.00) → countless New Yorkers`
7800	`- owners of Purdue (score: 0.00) → owner of Purdue`
7801	`- Sackler family (score: 0.00) → Sackler family`
7802	`- forget the masterminds (score: 0.01) → forget the mastermind`
7803	`- literally profited (score: 0.01) → literally profit`
7804	`- death of countless (score: 0.01)`
7805	`- family enterprise (score: 0.01)`
7806	`- family that literally (score: 0.03)`
7807	`- family (score: 0.03)`
7808	`- LETITIA (score: 0.04) → LETITIA`
7809	`- JAMES (score: 0.04) → JAMES`
7810	`- Yorkers (score: 0.04) → Yorkers`
7811	`- Purdue (score: 0.04) → Purdue`
7812	`- Sackler (score: 0.05) → Sackler`
7813	`- masterminds (score: 0.07) → mastermind`
7814	`- crisis (score: 0.07)`
7815	`- suffering (score: 0.07)`
7816	`- forget (score: 0.09)`
7817	`- enterprise (score: 0.09)`
7818	`Total keywords: 20 extracted in 0.0121 seconds`
7819
7820	`KeyBERT Keywords:`
7821	`- yorkers sackler family (score: 0.57)`
7822	`- family owners purdue (score: 0.57)`
7823	`- letitia james (score: 0.55)`
7824	`- letitia james let (score: 0.53)`
7825	`- sackler family (score: 0.51)`
7826	`- masterminds family (score: 0.50)`
7827	`- sackler family owners (score: 0.49)`
7828	`- forget masterminds family (score: 0.49)`
7829	`- new yorkers sackler (score: 0.48)`
7830	`- owners purdue (score: 0.48)`
7831	`- james let (score: 0.47)`
7832	`- yorkers sackler (score: 0.47)`
7833	`- masterminds family enterprise (score: 0.44)`
7834	`- james let forget (score: 0.44)`
7835	`- letitia (score: 0.43)`
7836	`- james (score: 0.41)`
7837	`- crisis family (score: 0.40)`
7838	`- crisis family literally (score: 0.40)`
7839	`- countless new yorkers (score: 0.39)`
7840	`- let forget masterminds (score: 0.39)`
7841	`Total keywords: 20 extracted in 0.0497 seconds`
7842
7843	`Dependency Relations (extracted in 0.0098sec):`
7844
7845	`Sentence 1: LETITIA JAMES:`
7846	`LETITIA (PROPN) --[compound]--> JAMES (PROPN)`
7847	`JAMES (PROPN) --[ROOT]--> JAMES (PROPN)`
7848	`: (PUNCT) --[punct]--> JAMES (PROPN)`
7849
7850	`Sentence 2: But let us not forget the masterminds, the family enterprise behind this crisis, the family that literally profited off of the suffering, the death of countless New Yorkers - the Sackler family, the owners of Purdue.`
7851	`But (CCONJ) --[cc]--> let (VERB)`
7852	`let (VERB) --[ROOT]--> let (VERB)`
7853	`us (PRON) --[nsubj]--> forget (VERB)`
7854	`not (PART) --[neg]--> forget (VERB)`
7855	`forget (VERB) --[ccomp]--> let (VERB)`
7856	`the (DET) --[det]--> masterminds (NOUN)`
7857	`masterminds (NOUN) --[dobj]--> forget (VERB)`
7858	`, (PUNCT) --[punct]--> forget (VERB)`
7859	`the (DET) --[det]--> enterprise (NOUN)`
7860	`family (NOUN) --[compound]--> enterprise (NOUN)`
7861	`enterprise (NOUN) --[conj]--> forget (VERB)`
7862	`behind (ADP) --[prep]--> enterprise (NOUN)`
7863	`this (DET) --[det]--> crisis (NOUN)`
7864	`crisis (NOUN) --[pobj]--> behind (ADP)`
7865	`, (PUNCT) --[punct]--> enterprise (NOUN)`
7866	`the (DET) --[det]--> family (NOUN)`
7867	`family (NOUN) --[appos]--> enterprise (NOUN)`
7868	`that (PRON) --[nsubj]--> profited (VERB)`
7869	`literally (ADV) --[advmod]--> profited (VERB)`
7870	`profited (VERB) --[relcl]--> family (NOUN)`
7871	`off (ADP) --[prep]--> profited (VERB)`
7872	`of (ADP) --[prep]--> off (ADP)`
7873	`the (DET) --[det]--> suffering (NOUN)`
7874	`suffering (NOUN) --[pobj]--> of (ADP)`
7875	`, (PUNCT) --[punct]--> family (NOUN)`
7876	`the (DET) --[det]--> death (NOUN)`
7877	`death (NOUN) --[conj]--> family (NOUN)`
7878	`of (ADP) --[prep]--> death (NOUN)`
7879	`countless (ADJ) --[amod]--> Yorkers (PROPN)`
7880	`New (PROPN) --[compound]--> Yorkers (PROPN)`
7881	`Yorkers (PROPN) --[pobj]--> of (ADP)`
7882	`- (PUNCT) --[punct]--> Yorkers (PROPN)`
7883	`the (DET) --[det]--> family (NOUN)`
7884	`Sackler (PROPN) --[compound]--> family (NOUN)`
7885	`family (NOUN) --[appos]--> Yorkers (PROPN)`
7886	`, (PUNCT) --[punct]--> Yorkers (PROPN)`
7887	`the (DET) --[det]--> owners (NOUN)`
7888	`owners (NOUN) --[appos]--> Yorkers (PROPN)`
7889	`of (ADP) --[prep]--> owners (NOUN)`
7890	`Purdue (PROPN) --[pobj]--> of (ADP)`
7891	`. (PUNCT) --[punct]--> let (VERB)`
7892
7893	`Total sentences: 2`
7894
7895	`RAKE Keyphrase Relationships:`
7896	`noun phrase: "the Sackler family" contains [sackler family], [family]`
7897	`verb phrase: "forget not masterminds" contains [masterminds], [forget]`
7898	`Total relationships found: 2`
7899
7900	`YAKE Keyphrase Relationships:`
7901	`noun phrase: "LETITIA JAMES" contains [letitia james], [letitia], [james]`
7902	`noun phrase: "countless New Yorkers" contains [countless new yorkers], [yorkers]`
7903	`noun phrase: "the Sackler family" contains [sackler family], [family], [sackler]`
7904	`verb phrase: "forget not masterminds" contains [masterminds], [forget]`
7905	`Total relationships found: 4`
7906
7907	`Timing Comparison:`
7908	`RAKE: 0.0000sec`
7909	`YAKE: 0.0121sec`
7910	`KeyBERT: 0.0497sec`
7911	`Dependencies: 0.0098sec`
7912	`Fastest: RAKE`
7913
7914	`================================================================================`
7915	`Message 59:`
7916	`MARA LIASSON: Hi, Ari.`
7917	`--------------------------------------------------------------------------------`
7918	`RAKE Keywords:`
7919	`- mara liasson (score: 4.00) → MARA liasson`
7920	`- hi (score: 1.00)`
7921	`- ari (score: 1.00) → Ari`
7922	`Total keywords: 3 extracted in 0.0000 seconds`
7923
7924	`YAKE Keywords:`
7925	`- MARA LIASSON (score: 0.01) → MARA liasson`
7926	`- Ari (score: 0.03) → Ari`
7927	`- MARA (score: 0.09) → MARA`
7928	`- LIASSON (score: 0.09)`
7929	`Total keywords: 4 extracted in 0.0000 seconds`
7930
7931	`KeyBERT Keywords:`
7932	`- liasson hi ari (score: 0.85)`
7933	`- mara liasson hi (score: 0.83)`
7934	`- mara liasson (score: 0.73)`
7935	`- liasson hi (score: 0.67)`
7936	`- hi ari (score: 0.63)`
7937	`- ari (score: 0.58)`
7938	`- mara (score: 0.57)`
7939	`- liasson (score: 0.54)`
7940	`- hi (score: 0.41)`
7941	`Total keywords: 9 extracted in 0.0178 seconds`
7942
7943	`Dependency Relations (extracted in 0.0060sec):`
7944
7945	`Sentence 1: MARA LIASSON: Hi, Ari.`
7946	`MARA (PROPN) --[nsubj]--> LIASSON (ADV)`
7947	`LIASSON (ADV) --[ROOT]--> LIASSON (ADV)`
7948	`: (PUNCT) --[punct]--> LIASSON (ADV)`
7949	`Hi (INTJ) --[dep]--> LIASSON (ADV)`
7950	`, (PUNCT) --[punct]--> Hi (INTJ)`
7951	`Ari (PROPN) --[npadvmod]--> Hi (INTJ)`
7952	`. (PUNCT) --[punct]--> LIASSON (ADV)`
7953
7954	`Total sentences: 1`
7955
7956	`RAKE Keyphrase Relationships:`
7957	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
7958
7959	`YAKE Keyphrase Relationships:`
7960	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
7961
7962	`Timing Comparison:`
7963	`RAKE: 0.0000sec`
7964	`YAKE: 0.0000sec`
7965	`KeyBERT: 0.0178sec`
7966	`Dependencies: 0.0060sec`
7967	`Fastest: RAKE`
7968
7969	`================================================================================`
7970	`Message 60:`
7971	`MARTIN: That's Nathan.`
7972	`--------------------------------------------------------------------------------`
7973	`RAKE Keywords:`
7974	`- nathan (score: 1.00) → Nathan`
7975	`- martin (score: 1.00) → MARTIN`
7976	`Total keywords: 2 extracted in 0.0000 seconds`
7977
7978	`YAKE Keywords:`
7979	`- MARTIN (score: 0.03) → MARTIN`
7980	`- Nathan (score: 0.09) → Nathan`
7981	`Total keywords: 2 extracted in 0.0000 seconds`
7982
7983	`KeyBERT Keywords:`
7984	`- martin nathan (score: 0.81)`
7985	`- nathan (score: 0.71)`
7986	`- martin (score: 0.58)`
7987	`Total keywords: 3 extracted in 0.0155 seconds`
7988
7989	`Dependency Relations (extracted in 0.0040sec):`
7990
7991	`Sentence 1: MARTIN: That's Nathan.`
7992	`MARTIN (PROPN) --[dep]--> 's (AUX)`
7993	`: (PUNCT) --[punct]--> MARTIN (PROPN)`
7994	`That (PRON) --[nsubj]--> 's (AUX)`
7995	`'s (AUX) --[ROOT]--> 's (AUX)`
7996	`Nathan (PROPN) --[attr]--> 's (AUX)`
7997	`. (PUNCT) --[punct]--> 's (AUX)`
7998
7999	`Total sentences: 1`
8000
8001	`RAKE Keyphrase Relationships:`
8002	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
8003
8004	`YAKE Keyphrase Relationships:`
8005	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
8006
8007	`Timing Comparison:`
8008	`RAKE: 0.0000sec`
8009	`YAKE: 0.0000sec`
8010	`KeyBERT: 0.0155sec`
8011	`Dependencies: 0.0040sec`
8012	`Fastest: RAKE`
8013
8014	`================================================================================`
8015	`Message 61:`
8016	`PETER ENNS: Our data suggests the opposite effect among those who oppose Trump and a disproportionate negative effect among those who identify as independent.`
8017	`--------------------------------------------------------------------------------`
8018	`RAKE Keywords:`
8019	`- opposite effect among (score: 9.00)`
8020	`- peter enns (score: 4.00) → PETER ENNS`
8021	`- oppose trump (score: 4.00) → oppose Trump`
8022	`- data suggests (score: 4.00) → datum suggest`
8023	`- independent (score: 1.00)`
8024	`- identify (score: 1.00)`
8025	`Total keywords: 6 extracted in 0.0000 seconds`
8026
8027	`YAKE Keywords:`
8028	`- PETER ENNS (score: 0.00) → PETER ENNS`
8029	`- disproportionate negative effect (score: 0.01)`
8030	`- oppose Trump (score: 0.02) → oppose Trump`
8031	`- identify as independent (score: 0.02)`
8032	`- data suggests (score: 0.03) → datum suggest`
8033	`- suggests the opposite (score: 0.03) → suggest the opposite`
8034	`- disproportionate negative (score: 0.03)`
8035	`- opposite effect (score: 0.04)`
8036	`- negative effect (score: 0.04)`
8037	`- PETER (score: 0.06) → PETER`
8038	`- ENNS (score: 0.06) → ENNS`
8039	`- Trump (score: 0.09) → Trump`
8040	`- effect (score: 0.10)`
8041	`- independent (score: 0.11)`
8042	`- data (score: 0.18) → datum`
8043	`- suggests (score: 0.18) → suggest`
8044	`- opposite (score: 0.18)`
8045	`- oppose (score: 0.18)`
8046	`- disproportionate (score: 0.18)`
8047	`- negative (score: 0.18)`
8048	`Total keywords: 20 extracted in 0.0168 seconds`
8049
8050	`KeyBERT Keywords:`
8051	`- effect oppose trump (score: 0.67)`
8052	`- oppose trump disproportionate (score: 0.57)`
8053	`- oppose trump (score: 0.55)`
8054	`- trump disproportionate negative (score: 0.55)`
8055	`- opposite effect oppose (score: 0.54)`
8056	`- effect oppose (score: 0.53)`
8057	`- negative effect identify (score: 0.47)`
8058	`- disproportionate negative effect (score: 0.47)`
8059	`- negative effect (score: 0.47)`
8060	`- suggests opposite effect (score: 0.45)`
8061	`- effect identify independent (score: 0.44)`
8062	`- opposite effect (score: 0.44)`
8063	`- oppose (score: 0.44)`
8064	`- data suggests opposite (score: 0.43)`
8065	`- trump disproportionate (score: 0.43)`
8066	`- identify independent (score: 0.41)`
8067	`- independent (score: 0.40)`
8068	`- suggests opposite (score: 0.38)`
8069	`- peter enns (score: 0.37)`
8070	`- trump (score: 0.36)`
8071	`Total keywords: 20 extracted in 0.0387 seconds`
8072
8073	`Dependency Relations (extracted in 0.0040sec):`
8074
8075	`Sentence 1: PETER ENNS:`
8076	`PETER (PROPN) --[compound]--> ENNS (PROPN)`
8077	`ENNS (PROPN) --[ROOT]--> ENNS (PROPN)`
8078	`: (PUNCT) --[punct]--> ENNS (PROPN)`
8079
8080	`Sentence 2: Our data suggests the opposite effect among those who oppose Trump and a disproportionate negative effect among those who identify as independent.`
8081	`Our (PRON) --[poss]--> data (NOUN)`
8082	`data (NOUN) --[nsubj]--> suggests (VERB)`
8083	`suggests (VERB) --[ROOT]--> suggests (VERB)`
8084	`the (DET) --[det]--> effect (NOUN)`
8085	`opposite (ADJ) --[amod]--> effect (NOUN)`
8086	`effect (NOUN) --[dobj]--> suggests (VERB)`
8087	`among (ADP) --[prep]--> effect (NOUN)`
8088	`those (PRON) --[pobj]--> among (ADP)`
8089	`who (PRON) --[nsubj]--> oppose (VERB)`
8090	`oppose (VERB) --[relcl]--> those (PRON)`
8091	`Trump (PROPN) --[dobj]--> oppose (VERB)`
8092	`and (CCONJ) --[cc]--> Trump (PROPN)`
8093	`a (DET) --[det]--> effect (NOUN)`
8094	`disproportionate (ADJ) --[advmod]--> negative (ADJ)`
8095	`negative (ADJ) --[amod]--> effect (NOUN)`
8096	`effect (NOUN) --[conj]--> Trump (PROPN)`
8097	`among (ADP) --[prep]--> oppose (VERB)`
8098	`those (PRON) --[pobj]--> among (ADP)`
8099	`who (PRON) --[nsubj]--> identify (VERB)`
8100	`identify (VERB) --[relcl]--> those (PRON)`
8101	`as (ADP) --[prep]--> identify (VERB)`
8102	`independent (ADJ) --[amod]--> as (ADP)`
8103	`. (PUNCT) --[punct]--> suggests (VERB)`
8104
8105	`Total sentences: 2`
8106
8107	`RAKE Keyphrase Relationships:`
8108	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
8109
8110	`YAKE Keyphrase Relationships:`
8111	`noun phrase: "PETER ENNS" contains [peter enns], [peter], [enns]`
8112	`noun phrase: "the opposite effect" contains [opposite effect], [effect], [opposite]`
8113	`noun phrase: "a disproportionate negative effect" contains [disproportionate negative effect], [disproportionate negative], [negative effect], [effect], [disproportionate], [negative]`
8114	`verb phrase: "suggests effect" contains [effect], [suggests]`
8115	`verb phrase: "oppose Trump among" contains [oppose trump], [trump], [oppose]`
8116	`Total relationships found: 5`
8117
8118	`Timing Comparison:`
8119	`RAKE: 0.0000sec`
8120	`YAKE: 0.0168sec`
8121	`KeyBERT: 0.0387sec`
8122	`Dependencies: 0.0040sec`
8123	`Fastest: RAKE`
8124
8125	`================================================================================`
8126	`Message 62:`
8127	`FLAHIVE: Attorney Paul Yetter represents current and former foster youth in the ongoing federal lawsuit against Texas' system.`
8128	`--------------------------------------------------------------------------------`
8129	`RAKE Keywords:`
8130	`- ongoing federal lawsuit (score: 9.00)`
8131	`- former foster youth (score: 9.00)`
8132	`- texas (score: 1.00) → Texas`
8133	`- system (score: 1.00)`
8134	`- flahive (score: 1.00) → FLAHIVE`
8135	`Total keywords: 5 extracted in 0.0000 seconds`
8136
8137	`YAKE Keywords:`
8138	`- Attorney Paul Yetter (score: 0.00) → Attorney Paul Yetter`
8139	`- Paul Yetter represents (score: 0.01) → Paul Yetter represent`
8140	`- Attorney Paul (score: 0.01) → Attorney Paul`
8141	`- Yetter represents current (score: 0.02) → Yetter represent current`
8142	`- Paul Yetter (score: 0.02) → Paul Yetter`
8143	`- Texas' system (score: 0.02)`
8144	`- FLAHIVE (score: 0.03) → FLAHIVE`
8145	`- ongoing federal lawsuit (score: 0.03)`
8146	`- Yetter represents (score: 0.04) → Yetter represent`
8147	`- lawsuit against Texas' (score: 0.04)`
8148	`- Attorney (score: 0.09) → Attorney`
8149	`- represents current (score: 0.10) → represent current`
8150	`- foster youth (score: 0.10)`
8151	`- ongoing federal (score: 0.10)`
8152	`- federal lawsuit (score: 0.10)`
8153	`- Paul (score: 0.14) → Paul`
8154	`- Yetter (score: 0.14) → Yetter`
8155	`- Texas' (score: 0.14)`
8156	`- system (score: 0.16)`
8157	`- represents (score: 0.30) → represent`
8158	`Total keywords: 20 extracted in 0.0232 seconds`
8159
8160	`KeyBERT Keywords:`
8161	`- attorney paul yetter (score: 0.71)`
8162	`- paul yetter represents (score: 0.63)`
8163	`- paul yetter (score: 0.62)`
8164	`- yetter (score: 0.55)`
8165	`- yetter represents (score: 0.55)`
8166	`- federal lawsuit texas (score: 0.51)`
8167	`- flahive attorney paul (score: 0.51)`
8168	`- current foster youth (score: 0.50)`
8169	`- foster youth ongoing (score: 0.47)`
8170	`- foster youth (score: 0.47)`
8171	`- yetter represents current (score: 0.47)`
8172	`- current foster (score: 0.46)`
8173	`- lawsuit texas (score: 0.46)`
8174	`- foster (score: 0.46)`
8175	`- represents current foster (score: 0.45)`
8176	`- ongoing federal lawsuit (score: 0.44)`
8177	`- youth ongoing federal (score: 0.44)`
8178	`- flahive attorney (score: 0.43)`
8179	`- federal lawsuit (score: 0.43)`
8180	`- attorney paul (score: 0.39)`
8181	`Total keywords: 20 extracted in 0.0349 seconds`
8182
8183	`Dependency Relations (extracted in 0.0060sec):`
8184
8185	`Sentence 1: FLAHIVE: Attorney Paul Yetter represents current and former foster youth in the ongoing federal lawsuit against Texas' system.`
8186	`FLAHIVE (PROPN) --[ROOT]--> FLAHIVE (PROPN)`
8187	`: (PUNCT) --[punct]--> FLAHIVE (PROPN)`
8188	`Attorney (PROPN) --[compound]--> Yetter (PROPN)`
8189	`Paul (PROPN) --[compound]--> Yetter (PROPN)`
8190	`Yetter (PROPN) --[nsubj]--> represents (VERB)`
8191	`represents (VERB) --[acl]--> FLAHIVE (PROPN)`
8192	`current (ADJ) --[amod]--> youth (NOUN)`
8193	`and (CCONJ) --[cc]--> current (ADJ)`
8194	`former (ADJ) --[conj]--> current (ADJ)`
8195	`foster (ADJ) --[amod]--> youth (NOUN)`
8196	`youth (NOUN) --[dobj]--> represents (VERB)`
8197	`in (ADP) --[prep]--> represents (VERB)`
8198	`the (DET) --[det]--> lawsuit (NOUN)`
8199	`ongoing (ADJ) --[amod]--> lawsuit (NOUN)`
8200	`federal (ADJ) --[amod]--> lawsuit (NOUN)`
8201	`lawsuit (NOUN) --[pobj]--> in (ADP)`
8202	`against (ADP) --[prep]--> lawsuit (NOUN)`
8203	`Texas (PROPN) --[poss]--> system (NOUN)`
8204	`' (PART) --[case]--> Texas (PROPN)`
8205	`system (NOUN) --[pobj]--> against (ADP)`
8206	`. (PUNCT) --[punct]--> represents (VERB)`
8207
8208	`Total sentences: 1`
8209
8210	`RAKE Keyphrase Relationships:`
8211	`noun phrase: "Texas' system" contains [texas], [system]`
8212	`Total relationships found: 1`
8213
8214	`YAKE Keyphrase Relationships:`
8215	`noun phrase: "Attorney Paul Yetter" contains [attorney paul yetter], [attorney paul], [paul yetter], [attorney], [paul], [yetter]`
8216	`noun phrase: "the ongoing federal lawsuit" contains [ongoing federal lawsuit], [ongoing federal], [federal lawsuit]`
8217	`noun phrase: "Texas' system" contains [texas' system], [texas'], [system]`
8218	`Total relationships found: 3`
8219
8220	`Timing Comparison:`
8221	`RAKE: 0.0000sec`
8222	`YAKE: 0.0232sec`
8223	`KeyBERT: 0.0349sec`
8224	`Dependencies: 0.0060sec`
8225	`Fastest: RAKE`
8226
8227	`================================================================================`
8228	`Message 63:`
8229	`ADRIANA LAFONT: I lived in that building for 10 years. My kids were born there. They learned how to swim in that pool. We had birthday parties, first communions, baptism - so many, so many memories.`
8230	`--------------------------------------------------------------------------------`
8231	`RAKE Keywords:`
8232	`- first communions (score: 4.00) → first communion`
8233	`- birthday parties (score: 4.00) → birthday party`
8234	`- adriana lafont (score: 4.00) → ADRIANA LAFONT`
8235	`- 10 years (score: 4.00) → 10 year`
8236	`- many memories (score: 3.50) → many memory`
8237	`- many (score: 1.50)`
8238	`- swim (score: 1.00)`
8239	`- pool (score: 1.00)`
8240	`- lived (score: 1.00) → live`
8241	`- learned (score: 1.00) → learn`
8242	`- kids (score: 1.00) → kid`
8243	`- building (score: 1.00)`
8244	`- born (score: 1.00) → bear`
8245	`- baptism (score: 1.00)`
8246	`Total keywords: 14 extracted in 0.0000 seconds`
8247
8248	`YAKE Keywords:`
8249	`- ADRIANA LAFONT (score: 0.01) → ADRIANA LAFONT`
8250	`- years (score: 0.07) → year`
8251	`- ADRIANA (score: 0.07) → ADRIANA`
8252	`- LAFONT (score: 0.07) → LAFONT`
8253	`- lived (score: 0.23) → live`
8254	`- building (score: 0.23)`
8255	`- baptism (score: 0.32)`
8256	`- kids were born (score: 0.35) → kid be bear`
8257	`- pool (score: 0.46)`
8258	`- birthday parties (score: 0.50) → birthday party`
8259	`- kids (score: 0.51) → kid`
8260	`- born (score: 0.51) → bear`
8261	`- parties (score: 0.51) → party`
8262	`- communions (score: 0.51) → communion`
8263	`- memories (score: 0.51) → memory`
8264	`- learned (score: 0.60) → learn`
8265	`- swim (score: 0.60)`
8266	`- birthday (score: 0.65)`
8267	`Total keywords: 18 extracted in 0.0030 seconds`
8268
8269	`KeyBERT Keywords:`
8270	`- lived building (score: 0.55)`
8271	`- lived building 10 (score: 0.52)`
8272	`- lafont lived building (score: 0.49)`
8273	`- baptism memories (score: 0.47)`
8274	`- born learned swim (score: 0.45)`
8275	`- swim pool birthday (score: 0.44)`
8276	`- learned swim pool (score: 0.43)`
8277	`- adriana lafont lived (score: 0.41)`
8278	`- pool birthday (score: 0.40)`
8279	`- pool birthday parties (score: 0.40)`
8280	`- swim pool (score: 0.39)`
8281	`- communions baptism memories (score: 0.38)`
8282	`- years kids born (score: 0.38)`
8283	`- memories (score: 0.36)`
8284	`- kids born (score: 0.36)`
8285	`- learned swim (score: 0.36)`
8286	`- pool (score: 0.36)`
8287	`- lived (score: 0.35)`
8288	`- building (score: 0.35)`
8289	`- birthday parties communions (score: 0.35)`
8290	`Total keywords: 20 extracted in 0.0453 seconds`
8291
8292	`Dependency Relations (extracted in 0.0132sec):`
8293
8294	`Sentence 1: ADRIANA LAFONT: I lived in that building for 10 years.`
8295	`ADRIANA (PROPN) --[compound]--> LAFONT (PROPN)`
8296	`LAFONT (PROPN) --[dep]--> lived (VERB)`
8297	`: (PUNCT) --[punct]--> lived (VERB)`
8298	`I (PRON) --[nsubj]--> lived (VERB)`
8299	`lived (VERB) --[ROOT]--> lived (VERB)`
8300	`in (ADP) --[prep]--> lived (VERB)`
8301	`that (DET) --[det]--> building (NOUN)`
8302	`building (NOUN) --[pobj]--> in (ADP)`
8303	`for (ADP) --[prep]--> lived (VERB)`
8304	`10 (NUM) --[nummod]--> years (NOUN)`
8305	`years (NOUN) --[pobj]--> for (ADP)`
8306	`. (PUNCT) --[punct]--> lived (VERB)`
8307
8308	`Sentence 2: My kids were born there.`
8309	`My (PRON) --[poss]--> kids (NOUN)`
8310	`kids (NOUN) --[nsubjpass]--> born (VERB)`
8311	`were (AUX) --[auxpass]--> born (VERB)`
8312	`born (VERB) --[ROOT]--> born (VERB)`
8313	`there (ADV) --[advmod]--> born (VERB)`
8314	`. (PUNCT) --[punct]--> born (VERB)`
8315
8316	`Sentence 3: They learned how to swim in that pool.`
8317	`They (PRON) --[nsubj]--> learned (VERB)`
8318	`learned (VERB) --[ROOT]--> learned (VERB)`
8319	`how (SCONJ) --[advmod]--> swim (VERB)`
8320	`to (PART) --[aux]--> swim (VERB)`
8321	`swim (VERB) --[xcomp]--> learned (VERB)`
8322	`in (ADP) --[prep]--> swim (VERB)`
8323	`that (DET) --[det]--> pool (NOUN)`
8324	`pool (NOUN) --[pobj]--> in (ADP)`
8325	`. (PUNCT) --[punct]--> learned (VERB)`
8326
8327	`Sentence 4: We had birthday parties, first communions, baptism - so many, so many memories.`
8328	`We (PRON) --[nsubj]--> had (VERB)`
8329	`had (VERB) --[ROOT]--> had (VERB)`
8330	`birthday (NOUN) --[compound]--> parties (NOUN)`
8331	`parties (NOUN) --[dobj]--> had (VERB)`
8332	`, (PUNCT) --[punct]--> parties (NOUN)`
8333	`first (ADJ) --[amod]--> communions (NOUN)`
8334	`communions (NOUN) --[appos]--> parties (NOUN)`
8335	`, (PUNCT) --[punct]--> communions (NOUN)`
8336	`baptism (NOUN) --[conj]--> communions (NOUN)`
8337	`- (PUNCT) --[punct]--> baptism (NOUN)`
8338	`so (ADV) --[advmod]--> many (ADJ)`
8339	`many (ADJ) --[amod]--> baptism (NOUN)`
8340	`, (PUNCT) --[punct]--> baptism (NOUN)`
8341	`so (ADV) --[advmod]--> many (ADJ)`
8342	`many (ADJ) --[amod]--> memories (NOUN)`
8343	`memories (NOUN) --[dobj]--> had (VERB)`
8344	`. (PUNCT) --[punct]--> had (VERB)`
8345
8346	`Total sentences: 4`
8347
8348	`RAKE Keyphrase Relationships:`
8349	`noun phrase: "so many memories" contains [many memories], [many]`
8350	`Total relationships found: 1`
8351
8352	`YAKE Keyphrase Relationships:`
8353	`noun phrase: "birthday parties" contains [birthday parties], [parties], [birthday]`
8354	`verb phrase: "had parties memories" contains [parties], [memories]`
8355	`Total relationships found: 2`
8356
8357	`Timing Comparison:`
8358	`RAKE: 0.0000sec`
8359	`YAKE: 0.0030sec`
8360	`KeyBERT: 0.0453sec`
8361	`Dependencies: 0.0132sec`
8362	`Fastest: RAKE`
8363
8364	`================================================================================`
8365	`Message 64:`
8366	`SHAPIRO: Congressman Mac Thornberry, Republican of Texas and ranking member of the House Armed Services Committee, thank you so much for your time today.`
8367	`--------------------------------------------------------------------------------`
8368	`RAKE Keywords:`
8369	`- congressman mac thornberry (score: 9.00) → Congressman Mac Thornberry`
8370	`- time today (score: 4.00)`
8371	`- ranking member (score: 4.00)`
8372	`- thank (score: 1.00)`
8373	`- texas (score: 1.00) → Texas`
8374	`- shapiro (score: 1.00) → SHAPIRO`
8375	`- republican (score: 1.00) → Republican`
8376	`- much (score: 1.00)`
8377	`Total keywords: 8 extracted in 0.0000 seconds`
8378
8379	`YAKE Keywords:`
8380	`- Congressman Mac Thornberry (score: 0.00) → Congressman Mac Thornberry`
8381	`- Armed Services Committee (score: 0.00) → Armed Services Committee`
8382	`- House Armed Services (score: 0.00) → House Armed Services`
8383	`- Congressman Mac (score: 0.00) → Congressman Mac`
8384	`- Mac Thornberry (score: 0.00) → Mac Thornberry`
8385	`- Republican of Texas (score: 0.00) → Republican of Texas`
8386	`- Services Committee (score: 0.00) → Services Committee`
8387	`- House Armed (score: 0.01) → House Armed`
8388	`- Armed Services (score: 0.01) → Armed Services`
8389	`- Texas and ranking (score: 0.01) → Texas and ranking`
8390	`- time today (score: 0.02)`
8391	`- ranking member (score: 0.03)`
8392	`- SHAPIRO (score: 0.03) → SHAPIRO`
8393	`- Congressman (score: 0.06) → Congressman`
8394	`- Thornberry (score: 0.06) → Thornberry`
8395	`- Republican (score: 0.06) → Republican`
8396	`- Committee (score: 0.06) → Committee`
8397	`- Mac (score: 0.09) → Mac`
8398	`- Texas (score: 0.09) → Texas`
8399	`- House (score: 0.09) → House`
8400	`Total keywords: 20 extracted in 0.0225 seconds`
8401
8402	`KeyBERT Keywords:`
8403	`- congressman mac thornberry (score: 0.78)`
8404	`- thornberry republican texas (score: 0.73)`
8405	`- mac thornberry republican (score: 0.71)`
8406	`- shapiro congressman mac (score: 0.69)`
8407	`- thornberry republican (score: 0.67)`
8408	`- shapiro congressman (score: 0.64)`
8409	`- congressman mac (score: 0.60)`
8410	`- mac thornberry (score: 0.59)`
8411	`- thornberry (score: 0.57)`
8412	`- congressman (score: 0.49)`
8413	`- republican texas (score: 0.47)`
8414	`- republican texas ranking (score: 0.45)`
8415	`- texas ranking member (score: 0.44)`
8416	`- shapiro (score: 0.42)`
8417	`- armed services committee (score: 0.40)`
8418	`- republican (score: 0.36)`
8419	`- member house armed (score: 0.35)`
8420	`- services committee thank (score: 0.33)`
8421	`- committee thank (score: 0.33)`
8422	`- texas ranking (score: 0.33)`
8423	`Total keywords: 20 extracted in 0.0314 seconds`
8424
8425	`Dependency Relations (extracted in 0.0060sec):`
8426
8427	`Sentence 1: SHAPIRO:`
8428	`SHAPIRO (PROPN) --[ROOT]--> SHAPIRO (PROPN)`
8429	`: (PUNCT) --[punct]--> SHAPIRO (PROPN)`
8430
8431	`Sentence 2: Congressman Mac Thornberry, Republican of Texas and ranking member of the House Armed Services Committee, thank you so much for your time today.`
8432	`Congressman (PROPN) --[compound]--> Thornberry (PROPN)`
8433	`Mac (PROPN) --[compound]--> Thornberry (PROPN)`
8434	`Thornberry (PROPN) --[nsubj]--> thank (VERB)`
8435	`, (PUNCT) --[punct]--> Thornberry (PROPN)`
8436	`Republican (PROPN) --[appos]--> Thornberry (PROPN)`
8437	`of (ADP) --[prep]--> Republican (PROPN)`
8438	`Texas (PROPN) --[pobj]--> of (ADP)`
8439	`and (CCONJ) --[cc]--> Republican (PROPN)`
8440	`ranking (ADJ) --[amod]--> member (NOUN)`
8441	`member (NOUN) --[conj]--> Republican (PROPN)`
8442	`of (ADP) --[prep]--> member (NOUN)`
8443	`the (DET) --[det]--> Committee (PROPN)`
8444	`House (PROPN) --[compound]--> Committee (PROPN)`
8445	`Armed (PROPN) --[compound]--> Services (PROPN)`
8446	`Services (PROPN) --[compound]--> Committee (PROPN)`
8447	`Committee (PROPN) --[pobj]--> of (ADP)`
8448	`, (PUNCT) --[punct]--> thank (VERB)`
8449	`thank (VERB) --[ROOT]--> thank (VERB)`
8450	`you (PRON) --[dobj]--> thank (VERB)`
8451	`so (ADV) --[advmod]--> much (ADV)`
8452	`much (ADV) --[advmod]--> thank (VERB)`
8453	`for (ADP) --[prep]--> thank (VERB)`
8454	`your (PRON) --[poss]--> time (NOUN)`
8455	`time (NOUN) --[pobj]--> for (ADP)`
8456	`today (NOUN) --[npadvmod]--> thank (VERB)`
8457	`. (PUNCT) --[punct]--> thank (VERB)`
8458
8459	`Total sentences: 2`
8460
8461	`RAKE Keyphrase Relationships:`
8462	`verb phrase: "thank you much for" contains [thank], [much]`
8463	`Total relationships found: 1`
8464
8465	`YAKE Keyphrase Relationships:`
8466	`noun phrase: "Congressman Mac Thornberry" contains [congressman mac thornberry], [congressman mac], [mac thornberry], [congressman], [thornberry], [mac]`
8467	`noun phrase: "the House Armed Services Committee" contains [armed services committee], [house armed services], [services committee], [house armed], [armed services], [committee], [house]`
8468	`Total relationships found: 2`
8469
8470	`Timing Comparison:`
8471	`RAKE: 0.0000sec`
8472	`YAKE: 0.0225sec`
8473	`KeyBERT: 0.0314sec`
8474	`Dependencies: 0.0060sec`
8475	`Fastest: RAKE`
8476
8477	`================================================================================`
8478	`Message 65:`
8479	`CASEY: Thanks, Michel.(SOUNDBITE OF NATHAN KAWANISHI'S "UENO")`
8480	`--------------------------------------------------------------------------------`
8481	`RAKE Keywords:`
8482	`- ueno ") (score: 4.00)`
8483	`- nathan kawanishi (score: 4.00) → NATHAN KAWANISHI`
8484	`- thanks (score: 1.00) → thank`
8485	`- soundbite (score: 1.00)`
8486	`- michel (score: 1.00)`
8487	`- casey (score: 1.00)`
8488	`Total keywords: 6 extracted in 0.0000 seconds`
8489
8490	`YAKE Keywords:`
8491	`- SOUNDBITE OF NATHAN (score: 0.01)`
8492	`- NATHAN KAWANISHI'S (score: 0.01)`
8493	`- CASEY (score: 0.03)`
8494	`- Michel. (score: 0.03)`
8495	`- UENO (score: 0.03) → UENO`
8496	`- SOUNDBITE (score: 0.09)`
8497	`- KAWANISHI'S (score: 0.09)`
8498	`- NATHAN (score: 0.14) → NATHAN`
8499	`Total keywords: 8 extracted in 0.0020 seconds`
8500
8501	`KeyBERT Keywords:`
8502	`- soundbite nathan kawanishi (score: 0.64)`
8503	`- michel soundbite nathan (score: 0.63)`
8504	`- casey thanks michel (score: 0.60)`
8505	`- nathan kawanishi ueno (score: 0.59)`
8506	`- soundbite nathan (score: 0.56)`
8507	`- michel soundbite (score: 0.53)`
8508	`- thanks michel soundbite (score: 0.51)`
8509	`- nathan kawanishi (score: 0.48)`
8510	`- casey thanks (score: 0.47)`
8511	`- kawanishi ueno (score: 0.46)`
8512	`- casey (score: 0.44)`
8513	`- thanks michel (score: 0.41)`
8514	`- michel (score: 0.39)`
8515	`- nathan (score: 0.39)`
8516	`- ueno (score: 0.35)`
8517	`- kawanishi (score: 0.34)`
8518	`- soundbite (score: 0.33)`
8519	`- thanks (score: 0.21)`
8520	`Total keywords: 18 extracted in 0.0217 seconds`
8521
8522	`Dependency Relations (extracted in 0.0040sec):`
8523
8524	`Sentence 1: CASEY: Thanks, Michel.(SOUNDBITE OF NATHAN KAWANISHI'S "UENO")`
8525	`CASEY (NOUN) --[ROOT]--> CASEY (NOUN)`
8526	`: (PUNCT) --[punct]--> CASEY (NOUN)`
8527	`Thanks (NOUN) --[appos]--> CASEY (NOUN)`
8528	`, (PUNCT) --[punct]--> Thanks (NOUN)`
8529	`Michel.(SOUNDBITE (PROPN) --[appos]--> Thanks (NOUN)`
8530	`OF (ADP) --[prep]--> Michel.(SOUNDBITE (PROPN)`
8531	`NATHAN (PROPN) --[compound]--> KAWANISHI (PROPN)`
8532	`KAWANISHI (PROPN) --[poss]--> UENO (PROPN)`
8533	`'S (PART) --[case]--> KAWANISHI (PROPN)`
8534	`" (PUNCT) --[punct]--> UENO (PROPN)`
8535	`UENO (PROPN) --[pobj]--> OF (ADP)`
8536	`" (PUNCT) --[punct]--> Thanks (NOUN)`
8537	`) (PUNCT) --[punct]--> Thanks (NOUN)`
8538
8539	`Total sentences: 1`
8540
8541	`RAKE Keyphrase Relationships:`
8542	`noun phrase: "Michel.(SOUNDBITE" contains [soundbite], [michel]`
8543	`Total relationships found: 1`
8544
8545	`YAKE Keyphrase Relationships:`
8546	`noun phrase: "Michel.(SOUNDBITE" contains [michel.], [soundbite]`
8547	`noun phrase: "NATHAN KAWANISHI'S "UENO" contains [nathan kawanishi's], [ueno], [kawanishi's], [nathan]`
8548	`Total relationships found: 2`
8549
8550	`Timing Comparison:`
8551	`RAKE: 0.0000sec`
8552	`YAKE: 0.0020sec`
8553	`KeyBERT: 0.0217sec`
8554	`Dependencies: 0.0040sec`
8555	`Fastest: RAKE`
8556
8557	`================================================================================`
8558	`Message 66:`
8559	`MARTIN: So using the gold standard - what did...`
8560	`--------------------------------------------------------------------------------`
8561	`RAKE Keywords:`
8562	`- gold standard (score: 4.00)`
8563	`- using (score: 1.00) → use`
8564	`- martin (score: 1.00) → MARTIN`
8565	`- ... (score: 1.00)`
8566	`Total keywords: 4 extracted in 0.0000 seconds`
8567
8568	`YAKE Keywords:`
8569	`- MARTIN (score: 0.03) → MARTIN`
8570	`- gold standard (score: 0.05)`
8571	`- standard (score: 0.16)`
8572	`- gold (score: 0.30)`
8573	`Total keywords: 4 extracted in 0.0000 seconds`
8574
8575	`KeyBERT Keywords:`
8576	`- martin using gold (score: 0.78)`
8577	`- gold standard (score: 0.72)`
8578	`- gold standard did (score: 0.72)`
8579	`- using gold standard (score: 0.70)`
8580	`- using gold (score: 0.57)`
8581	`- martin using (score: 0.54)`
8582	`- martin (score: 0.47)`
8583	`- gold (score: 0.45)`
8584	`- standard (score: 0.40)`
8585	`- standard did (score: 0.39)`
8586	`- using (score: 0.16)`
8587	`- did (score: 0.13)`
8588	`Total keywords: 12 extracted in 0.0158 seconds`
8589
8590	`Dependency Relations (extracted in 0.0060sec):`
8591
8592	`Sentence 1: MARTIN: So using the gold standard - what did...`
8593	`MARTIN (PROPN) --[dep]--> did (VERB)`
8594	`: (PUNCT) --[punct]--> MARTIN (PROPN)`
8595	`So (ADV) --[advmod]--> did (VERB)`
8596	`using (VERB) --[advcl]--> did (VERB)`
8597	`the (DET) --[det]--> standard (NOUN)`
8598	`gold (NOUN) --[compound]--> standard (NOUN)`
8599	`standard (NOUN) --[dobj]--> using (VERB)`
8600	`- (PUNCT) --[punct]--> did (VERB)`
8601	`what (PRON) --[nsubj]--> did (VERB)`
8602	`did (VERB) --[ROOT]--> did (VERB)`
8603	`... (PUNCT) --[punct]--> did (VERB)`
8604
8605	`Total sentences: 1`
8606
8607	`RAKE Keyphrase Relationships:`
8608	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
8609
8610	`YAKE Keyphrase Relationships:`
8611	`noun phrase: "the gold standard" contains [gold standard], [standard], [gold]`
8612	`Total relationships found: 1`
8613
8614	`Timing Comparison:`
8615	`RAKE: 0.0000sec`
8616	`YAKE: 0.0000sec`
8617	`KeyBERT: 0.0158sec`
8618	`Dependencies: 0.0060sec`
8619	`Fastest: RAKE`
8620
8621	`================================================================================`
8622	`Message 67:`
8623	`ROSENTHAL: You know, the first thing is always ask for an itemized bill. And if you see something that doesn't make sense, like this $5,000 charge that was listed as anesthesia - you know, she said, whoa, what's that about? Asking those questions got the charge dropped for her to about $500. So for me, the takeaway is, you know, we tend to look at these bills and go, whoa, what's that, I don't know - ask, make a fuss. It may make a difference, both for you and for your insurer.`
8624	`--------------------------------------------------------------------------------`
8625	`RAKE Keywords:`
8626	`- see something (score: 4.00)`
8627	`- questions got (score: 4.00) → question get`
8628	`- itemized bill (score: 4.00) → itemize bill`
8629	`- first thing (score: 4.00)`
8630	`- charge dropped (score: 4.00) → charge drop`
8631	`- 000 charge (score: 4.00)`
8632	`- may make (score: 3.67)`
8633	`- make sense (score: 3.67)`
8634	`- always ask (score: 3.50)`
8635	`- make (score: 1.67)`
8636	`- ask (score: 1.50)`
8637	`- whoa (score: 1.00)`
8638	`- whoa (score: 1.00)`
8639	`- tend (score: 1.00)`
8640	`- takeaway (score: 1.00)`
8641	`- said (score: 1.00) → say`
8642	`- rosenthal (score: 1.00) → ROSENTHAL`
8643	`- look (score: 1.00)`
8644	`- listed (score: 1.00) → list`
8645	`- like (score: 1.00)`
8646	`- know (score: 1.00)`
8647	`- know (score: 1.00)`
8648	`- know (score: 1.00)`
8649	`- know (score: 1.00)`
8650	`- insurer (score: 1.00)`
8651	`- go (score: 1.00)`
8652	`- fuss (score: 1.00)`
8653	`- difference (score: 1.00)`
8654	`- bills (score: 1.00) → bill`
8655	`- asking (score: 1.00) → ask`
8656	`- anesthesia (score: 1.00)`
8657	`- 500 (score: 1.00)`
8658	`- 5 (score: 1.00)`
8659	`Total keywords: 33 extracted in 0.0000 seconds`
8660
8661	`YAKE Keywords:`
8662	`- ROSENTHAL (score: 0.05) → ROSENTHAL`
8663	`- itemized bill (score: 0.10) → itemize bill`
8664	`- whoa (score: 0.12)`
8665	`- make (score: 0.17)`
8666	`- thing (score: 0.20)`
8667	`- itemized (score: 0.20) → itemize`
8668	`- listed as anesthesia (score: 0.21) → list as anesthesia`
8669	`- charge (score: 0.23)`
8670	`- make sense (score: 0.24)`
8671	`- charge dropped (score: 0.35) → charge drop`
8672	`- sense (score: 0.38)`
8673	`- anesthesia (score: 0.38)`
8674	`- listed (score: 0.46) → list`
8675	`- bill (score: 0.47)`
8676	`- bills (score: 0.47) → bill`
8677	`- fuss (score: 0.53)`
8678	`- make a fuss (score: 0.55)`
8679	`- questions (score: 0.55) → question`
8680	`- dropped (score: 0.55) → drop`
8681	`- difference (score: 0.56)`
8682	`Total keywords: 20 extracted in 0.0133 seconds`
8683
8684	`KeyBERT Keywords:`
8685	`- charge listed anesthesia (score: 0.72)`
8686	`- anesthesia (score: 0.44)`
8687	`- listed anesthesia (score: 0.43)`
8688	`- anesthesia know (score: 0.43)`
8689	`- questions got charge (score: 0.42)`
8690	`- 000 charge listed (score: 0.41)`
8691	`- insurer (score: 0.41)`
8692	`- listed anesthesia know (score: 0.41)`
8693	`- make difference insurer (score: 0.40)`
8694	`- anesthesia know said (score: 0.39)`
8695	`- 000 charge (score: 0.39)`
8696	`- like 000 charge (score: 0.38)`
8697	`- charge dropped 500 (score: 0.37)`
8698	`- charge listed (score: 0.37)`
8699	`- got charge dropped (score: 0.35)`
8700	`- difference insurer (score: 0.35)`
8701	`- charge dropped (score: 0.34)`
8702	`- charge (score: 0.32)`
8703	`- look bills (score: 0.31)`
8704	`- look bills whoa (score: 0.29)`
8705	`Total keywords: 20 extracted in 0.0688 seconds`
8706
8707	`Dependency Relations (extracted in 0.0287sec):`
8708
8709	`Sentence 1: ROSENTHAL:`
8710	`ROSENTHAL (NOUN) --[ROOT]--> ROSENTHAL (NOUN)`
8711	`: (PUNCT) --[punct]--> ROSENTHAL (NOUN)`
8712
8713	`Sentence 2: You know, the first thing is always ask for an itemized bill.`
8714	`You (PRON) --[nsubj]--> know (VERB)`
8715	`know (VERB) --[parataxis]--> ask (VERB)`
8716	`, (PUNCT) --[punct]--> ask (VERB)`
8717	`the (DET) --[det]--> thing (NOUN)`
8718	`first (ADJ) --[amod]--> thing (NOUN)`
8719	`thing (NOUN) --[nsubjpass]--> ask (VERB)`
8720	`is (AUX) --[auxpass]--> ask (VERB)`
8721	`always (ADV) --[advmod]--> ask (VERB)`
8722	`ask (VERB) --[ROOT]--> ask (VERB)`
8723	`for (ADP) --[prep]--> ask (VERB)`
8724	`an (DET) --[det]--> bill (NOUN)`
8725	`itemized (VERB) --[amod]--> bill (NOUN)`
8726	`bill (NOUN) --[pobj]--> for (ADP)`
8727	`. (PUNCT) --[punct]--> ask (VERB)`
8728
8729	`Sentence 3: And if you see something that doesn't make sense, like this $5,000 charge that was listed as anesthesia - you know, she said, whoa, what's that about?`
8730	`And (CCONJ) --[cc]--> 's (AUX)`
8731	`if (SCONJ) --[mark]--> see (VERB)`
8732	`you (PRON) --[nsubj]--> see (VERB)`
8733	`see (VERB) --[advcl]--> 's (AUX)`
8734	`something (PRON) --[dobj]--> see (VERB)`
8735	`that (PRON) --[nsubj]--> make (VERB)`
8736	`does (AUX) --[aux]--> make (VERB)`
8737	`n't (PART) --[neg]--> make (VERB)`
8738	`make (VERB) --[relcl]--> something (PRON)`
8739	`sense (NOUN) --[dobj]--> make (VERB)`
8740	`, (PUNCT) --[punct]--> 's (AUX)`
8741	`like (ADP) --[prep]--> 's (AUX)`
8742	`this (DET) --[det]--> charge (NOUN)`
8743	`$ (SYM) --[nmod]--> 5,000 (NUM)`
8744	`5,000 (NUM) --[nummod]--> charge (NOUN)`
8745	`charge (NOUN) --[pobj]--> like (ADP)`
8746	`that (PRON) --[nsubjpass]--> listed (VERB)`
8747	`was (AUX) --[auxpass]--> listed (VERB)`
8748	`listed (VERB) --[relcl]--> charge (NOUN)`
8749	`as (ADP) --[prep]--> listed (VERB)`
8750	`anesthesia (NOUN) --[pobj]--> as (ADP)`
8751	`- (PUNCT) --[punct]--> 's (AUX)`
8752	`you (PRON) --[nsubj]--> know (VERB)`
8753	`know (VERB) --[parataxis]--> 's (AUX)`
8754	`, (PUNCT) --[punct]--> said (VERB)`
8755	`she (PRON) --[nsubj]--> said (VERB)`
8756	`said (VERB) --[parataxis]--> know (VERB)`
8757	`, (PUNCT) --[punct]--> said (VERB)`
8758	`whoa (INTJ) --[dep]--> know (VERB)`
8759	`, (PUNCT) --[punct]--> whoa (INTJ)`
8760	`what (PRON) --[nsubj]--> 's (AUX)`
8761	`'s (AUX) --[ROOT]--> 's (AUX)`
8762	`that (PRON) --[nsubj]--> 's (AUX)`
8763	`about (ADP) --[prep]--> 's (AUX)`
8764	`? (PUNCT) --[punct]--> 's (AUX)`
8765
8766	`Sentence 4: Asking those questions got the charge dropped for her to about $500.`
8767	`Asking (VERB) --[csubj]--> got (VERB)`
8768	`those (DET) --[det]--> questions (NOUN)`
8769	`questions (NOUN) --[dobj]--> Asking (VERB)`
8770	`got (VERB) --[ROOT]--> got (VERB)`
8771	`the (DET) --[det]--> charge (NOUN)`
8772	`charge (NOUN) --[nsubj]--> dropped (VERB)`
8773	`dropped (VERB) --[ccomp]--> got (VERB)`
8774	`for (ADP) --[prep]--> dropped (VERB)`
8775	`her (PRON) --[pobj]--> for (ADP)`
8776	`to (ADP) --[prep]--> dropped (VERB)`
8777	`about (ADV) --[advmod]--> 500 (NUM)`
8778	`$ (SYM) --[quantmod]--> 500 (NUM)`
8779	`500 (NUM) --[pobj]--> to (ADP)`
8780	`. (PUNCT) --[punct]--> got (VERB)`
8781
8782	`Sentence 5: So for me, the takeaway is, you know, we tend to look at these bills and go, whoa, what's that, I don't know - ask, make a fuss.`
8783	`So (ADV) --[advmod]--> is (AUX)`
8784	`for (ADP) --[prep]--> is (AUX)`
8785	`me (PRON) --[pobj]--> for (ADP)`
8786	`, (PUNCT) --[punct]--> is (AUX)`
8787	`the (DET) --[det]--> takeaway (NOUN)`
8788	`takeaway (NOUN) --[nsubj]--> is (AUX)`
8789	`is (AUX) --[ROOT]--> is (AUX)`
8790	`, (PUNCT) --[punct]--> is (AUX)`
8791	`you (PRON) --[nsubj]--> know (VERB)`
8792	`know (VERB) --[parataxis]--> is (AUX)`
8793	`, (PUNCT) --[punct]--> is (AUX)`
8794	`we (PRON) --[nsubj]--> tend (VERB)`
8795	`tend (VERB) --[ccomp]--> is (AUX)`
8796	`to (PART) --[aux]--> look (VERB)`
8797	`look (VERB) --[xcomp]--> tend (VERB)`
8798	`at (ADP) --[prep]--> look (VERB)`
8799	`these (DET) --[det]--> bills (NOUN)`
8800	`bills (NOUN) --[pobj]--> at (ADP)`
8801	`and (CCONJ) --[cc]--> look (VERB)`
8802	`go (VERB) --[conj]--> look (VERB)`
8803	`, (PUNCT) --[punct]--> go (VERB)`
8804	`whoa (INTJ) --[dep]--> look (VERB)`
8805	`, (PUNCT) --[punct]--> 's (AUX)`
8806	`what (PRON) --[nsubj]--> 's (AUX)`
8807	`'s (AUX) --[parataxis]--> make (VERB)`
8808	`that (PRON) --[attr]--> 's (AUX)`
8809	`, (PUNCT) --[punct]--> 's (AUX)`
8810	`I (PRON) --[nsubj]--> know (VERB)`
8811	`do (AUX) --[aux]--> know (VERB)`
8812	`n't (PART) --[neg]--> know (VERB)`
8813	`know (VERB) --[prep]--> , (PUNCT)`
8814	`- (PUNCT) --[punct]--> ask (NOUN)`
8815	`ask (NOUN) --[dobj]--> know (VERB)`
8816	`, (PUNCT) --[punct]--> make (VERB)`
8817	`make (VERB) --[ccomp]--> is (AUX)`
8818	`a (DET) --[det]--> fuss (NOUN)`
8819	`fuss (NOUN) --[dobj]--> make (VERB)`
8820	`. (PUNCT) --[punct]--> is (AUX)`
8821
8822	`Sentence 6: It may make a difference, both for you and for your insurer.`
8823	`It (PRON) --[nsubj]--> make (VERB)`
8824	`may (AUX) --[aux]--> make (VERB)`
8825	`make (VERB) --[ROOT]--> make (VERB)`
8826	`a (DET) --[det]--> difference (NOUN)`
8827	`difference (NOUN) --[dobj]--> make (VERB)`
8828	`, (PUNCT) --[punct]--> make (VERB)`
8829	`both (PRON) --[preconj]--> for (ADP)`
8830	`for (ADP) --[prep]--> make (VERB)`
8831	`you (PRON) --[pobj]--> for (ADP)`
8832	`and (CCONJ) --[cc]--> for (ADP)`
8833	`for (ADP) --[conj]--> for (ADP)`
8834	`your (PRON) --[poss]--> insurer (NOUN)`
8835	`insurer (NOUN) --[pobj]--> for (ADP)`
8836	`. (PUNCT) --[punct]--> make (VERB)`
8837
8838	`Total sentences: 6`
8839
8840	`RAKE Keyphrase Relationships:`
8841	`noun phrase: "this $5,000 charge" contains [000 charge], [5]`
8842	`verb phrase: "Asking questions" contains [ask], [asking]`
8843	`verb phrase: "know do n't ask" contains [ask], [know], [know], [know], [know]`
8844	`verb phrase: "make fuss" contains [make], [fuss]`
8845	`verb phrase: "make may difference for" contains [make], [difference]`
8846	`Total relationships found: 5`
8847
8848	`YAKE Keyphrase Relationships:`
8849	`noun phrase: "an itemized bill" contains [itemized bill], [itemized], [bill]`
8850	`noun phrase: "these bills" contains [bill], [bills]`
8851	`verb phrase: "make does n't sense" contains [make], [sense]`
8852	`verb phrase: "make fuss" contains [make], [fuss]`
8853	`verb phrase: "make may difference for" contains [make], [difference]`
8854	`Total relationships found: 5`
8855
8856	`Timing Comparison:`
8857	`RAKE: 0.0000sec`
8858	`YAKE: 0.0133sec`
8859	`KeyBERT: 0.0688sec`
8860	`Dependencies: 0.0287sec`
8861	`Fastest: RAKE`
8862
8863	`================================================================================`
8864	`Message 68:`
8865	`WAYNE SANDERS: Opera Ebony started in this living room, literally. The table wasn't there. That chair wasn't there.`
8866	`--------------------------------------------------------------------------------`
8867	`RAKE Keywords:`
8868	`- opera ebony started (score: 9.00) → Opera Ebony start`
8869	`- wayne sanders (score: 4.00) → WAYNE sander`
8870	`- living room (score: 4.00)`
8871	`- table (score: 1.00)`
8872	`- literally (score: 1.00)`
8873	`- chair (score: 1.00)`
8874	`Total keywords: 6 extracted in 0.0000 seconds`
8875
8876	`YAKE Keywords:`
8877	`- Opera Ebony started (score: 0.00) → Opera Ebony start`
8878	`- WAYNE SANDERS (score: 0.00) → WAYNE sander`
8879	`- Opera Ebony (score: 0.01) → Opera Ebony`
8880	`- Ebony started (score: 0.02) → Ebony start`
8881	`- living room (score: 0.03)`
8882	`- literally (score: 0.07)`
8883	`- WAYNE (score: 0.07) → WAYNE`
8884	`- SANDERS (score: 0.07) → sander`
8885	`- Opera (score: 0.07) → Opera`
8886	`- Ebony (score: 0.10) → Ebony`
8887	`- room (score: 0.14)`
8888	`- started (score: 0.22) → start`
8889	`- living (score: 0.22)`
8890	`- table (score: 0.49)`
8891	`- chair (score: 0.59)`
8892	`Total keywords: 15 extracted in 0.0060 seconds`
8893
8894	`KeyBERT Keywords:`
8895	`- sanders opera ebony (score: 0.64)`
8896	`- table wasn chair (score: 0.58)`
8897	`- sanders opera (score: 0.56)`
8898	`- wayne sanders opera (score: 0.53)`
8899	`- opera ebony (score: 0.51)`
8900	`- room literally table (score: 0.51)`
8901	`- opera ebony started (score: 0.50)`
8902	`- wasn chair (score: 0.48)`
8903	`- ebony started living (score: 0.46)`
8904	`- chair wasn (score: 0.45)`
8905	`- chair (score: 0.45)`
8906	`- ebony started (score: 0.43)`
8907	`- wasn chair wasn (score: 0.42)`
8908	`- table wasn (score: 0.40)`
8909	`- ebony (score: 0.40)`
8910	`- room literally (score: 0.39)`
8911	`- living room literally (score: 0.38)`
8912	`- living room (score: 0.38)`
8913	`- opera (score: 0.38)`
8914	`- literally table wasn (score: 0.38)`
8915	`Total keywords: 20 extracted in 0.0227 seconds`
8916
8917	`Dependency Relations (extracted in 0.0057sec):`
8918
8919	`Sentence 1: WAYNE SANDERS:`
8920	`WAYNE (PROPN) --[compound]--> SANDERS (NOUN)`
8921	`SANDERS (NOUN) --[ROOT]--> SANDERS (NOUN)`
8922	`: (PUNCT) --[punct]--> SANDERS (NOUN)`
8923
8924	`Sentence 2: Opera Ebony started in this living room, literally.`
8925	`Opera (PROPN) --[compound]--> Ebony (PROPN)`
8926	`Ebony (PROPN) --[nsubj]--> started (VERB)`
8927	`started (VERB) --[ROOT]--> started (VERB)`
8928	`in (ADP) --[prep]--> started (VERB)`
8929	`this (DET) --[det]--> room (NOUN)`
8930	`living (NOUN) --[compound]--> room (NOUN)`
8931	`room (NOUN) --[pobj]--> in (ADP)`
8932	`, (PUNCT) --[punct]--> started (VERB)`
8933	`literally (ADV) --[advmod]--> started (VERB)`
8934	`. (PUNCT) --[punct]--> started (VERB)`
8935
8936	`Sentence 3: The table wasn't there.`
8937	`The (DET) --[det]--> table (NOUN)`
8938	`table (NOUN) --[nsubj]--> was (AUX)`
8939	`was (AUX) --[ROOT]--> was (AUX)`
8940	`n't (PART) --[neg]--> was (AUX)`
8941	`there (ADV) --[advmod]--> was (AUX)`
8942	`. (PUNCT) --[punct]--> was (AUX)`
8943
8944	`Sentence 4: That chair wasn't there.`
8945	`That (DET) --[det]--> chair (NOUN)`
8946	`chair (NOUN) --[nsubj]--> was (AUX)`
8947	`was (AUX) --[ROOT]--> was (AUX)`
8948	`n't (PART) --[neg]--> was (AUX)`
8949	`there (ADV) --[advmod]--> was (AUX)`
8950	`. (PUNCT) --[punct]--> was (AUX)`
8951
8952	`Total sentences: 4`
8953
8954	`RAKE Keyphrase Relationships:`
8955	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
8956
8957	`YAKE Keyphrase Relationships:`
8958	`noun phrase: "WAYNE SANDERS" contains [wayne sanders], [wayne], [sanders]`
8959	`noun phrase: "Opera Ebony" contains [opera ebony], [opera], [ebony]`
8960	`noun phrase: "this living room" contains [living room], [room], [living]`
8961	`verb phrase: "started in literally" contains [literally], [started]`
8962	`Total relationships found: 4`
8963
8964	`Timing Comparison:`
8965	`RAKE: 0.0000sec`
8966	`YAKE: 0.0060sec`
8967	`KeyBERT: 0.0227sec`
8968	`Dependencies: 0.0057sec`
8969	`Fastest: RAKE`
8970
8971	`================================================================================`
8972	`Message 69:`
8973	`LIMBONG: Also coming out that same day is Attica Locke's "Guide Me Home." Locke is a longtime crime writer, and this latest book is the third in a trilogy starring Texas Detective Darren Mathews. It's that classic setup of, like, he's being pulled out of retirement to solve one more crime, you know what I mean?`
8974	`--------------------------------------------------------------------------------`
8975	`RAKE Keywords:`
8976	`- longtime crime writer (score: 8.00)`
8977	`- solve one (score: 4.00)`
8978	`- latest book (score: 4.00) → late book`
8979	`- home ." (score: 4.00)`
8980	`- classic setup (score: 4.00)`
8981	`- also coming (score: 4.00) → also come`
8982	`- attica locke (score: 3.50) → Attica Locke`
8983	`- crime (score: 2.00)`
8984	`- locke (score: 1.50) → Locke`
8985	`- third (score: 1.00)`
8986	`- retirement (score: 1.00)`
8987	`- pulled (score: 1.00) → pull`
8988	`- mean (score: 1.00)`
8989	`- limbong (score: 1.00) → LIMBONG`
8990	`- like (score: 1.00)`
8991	`- know (score: 1.00)`
8992	`- guide (score: 1.00) → Guide`
8993	`- day (score: 1.00)`
8994	`Total keywords: 18 extracted in 0.0000 seconds`
8995
8996	`YAKE Keywords:`
8997	`- Guide Me Home (score: 0.00) → Guide Me Home`
8998	`- Detective Darren Mathews (score: 0.01) → Detective Darren Mathews`
8999	`- day is Attica (score: 0.02) → day be Attica`
9000	`- Texas Detective Darren (score: 0.02) → Texas Detective Darren`
9001	`- Attica Locke (score: 0.02) → Attica Locke`
9002	`- starring Texas Detective (score: 0.04) → star Texas Detective`
9003	`- LIMBONG (score: 0.04) → LIMBONG`
9004	`- Darren Mathews (score: 0.05) → Darren Mathews`
9005	`- Guide (score: 0.07) → Guide`
9006	`- Home (score: 0.07) → Home`
9007	`- Texas Detective (score: 0.07) → Texas Detective`
9008	`- Detective Darren (score: 0.07) → Detective Darren`
9009	`- trilogy starring Texas (score: 0.08) → trilogy star Texas`
9010	`- Attica (score: 0.09) → Attica`
9011	`- Locke (score: 0.13) → Locke`
9012	`- starring Texas (score: 0.13) → star Texas`
9013	`- longtime crime writer (score: 0.14)`
9014	`- Mathews (score: 0.19) → Mathews`
9015	`- coming (score: 0.20) → come`
9016	`- day (score: 0.20)`
9017	`Total keywords: 20 extracted in 0.0195 seconds`
9018
9019	`KeyBERT Keywords:`
9020	`- crime writer latest (score: 0.57)`
9021	`- longtime crime writer (score: 0.54)`
9022	`- crime writer (score: 0.54)`
9023	`- locke longtime crime (score: 0.54)`
9024	`- latest book trilogy (score: 0.53)`
9025	`- attica locke guide (score: 0.53)`
9026	`- guide home locke (score: 0.53)`
9027	`- locke guide home (score: 0.53)`
9028	`- book trilogy (score: 0.50)`
9029	`- writer latest book (score: 0.49)`
9030	`- book trilogy starring (score: 0.49)`
9031	`- locke guide (score: 0.48)`
9032	`- home locke (score: 0.46)`
9033	`- latest book (score: 0.46)`
9034	`- day attica locke (score: 0.45)`
9035	`- home locke longtime (score: 0.44)`
9036	`- starring texas detective (score: 0.44)`
9037	`- attica locke (score: 0.43)`
9038	`- trilogy starring texas (score: 0.42)`
9039	`- texas detective (score: 0.41)`
9040	`Total keywords: 20 extracted in 0.0478 seconds`
9041
9042	`Dependency Relations (extracted in 0.0130sec):`
9043
9044	`Sentence 1: LIMBONG: Also coming out that same day is Attica Locke's "Guide Me Home."`
9045	`LIMBONG (PROPN) --[nsubj]--> coming (VERB)`
9046	`: (PUNCT) --[punct]--> LIMBONG (PROPN)`
9047	`Also (ADV) --[advmod]--> coming (VERB)`
9048	`coming (VERB) --[ROOT]--> coming (VERB)`
9049	`out (ADP) --[prt]--> coming (VERB)`
9050	`that (DET) --[det]--> day (NOUN)`
9051	`same (ADJ) --[amod]--> day (NOUN)`
9052	`day (NOUN) --[npadvmod]--> coming (VERB)`
9053	`is (AUX) --[aux]--> coming (VERB)`
9054	`Attica (PROPN) --[compound]--> Locke (PROPN)`
9055	`Locke (PROPN) --[poss]--> Home (PROPN)`
9056	`'s (PART) --[case]--> Locke (PROPN)`
9057	`" (PUNCT) --[punct]--> Home (PROPN)`
9058	`Guide (PROPN) --[compound]--> Home (PROPN)`
9059	`Me (PROPN) --[compound]--> Home (PROPN)`
9060	`Home (PROPN) --[attr]--> is (AUX)`
9061	`. (PUNCT) --[punct]--> coming (VERB)`
9062	`" (PUNCT) --[punct]--> coming (VERB)`
9063
9064	`Sentence 2: Locke is a longtime crime writer, and this latest book is the third in a trilogy starring Texas Detective Darren Mathews.`
9065	`Locke (PROPN) --[nsubj]--> is (AUX)`
9066	`is (AUX) --[ROOT]--> is (AUX)`
9067	`a (DET) --[det]--> writer (NOUN)`
9068	`longtime (ADJ) --[amod]--> writer (NOUN)`
9069	`crime (NOUN) --[compound]--> writer (NOUN)`
9070	`writer (NOUN) --[attr]--> is (AUX)`
9071	`, (PUNCT) --[punct]--> is (AUX)`
9072	`and (CCONJ) --[cc]--> is (AUX)`
9073	`this (DET) --[det]--> book (NOUN)`
9074	`latest (ADJ) --[amod]--> book (NOUN)`
9075	`book (NOUN) --[nsubj]--> is (AUX)`
9076	`is (AUX) --[conj]--> is (AUX)`
9077	`the (DET) --[det]--> third (ADJ)`
9078	`third (ADJ) --[attr]--> is (AUX)`
9079	`in (ADP) --[prep]--> third (ADJ)`
9080	`a (DET) --[det]--> trilogy (NOUN)`
9081	`trilogy (NOUN) --[pobj]--> in (ADP)`
9082	`starring (VERB) --[acl]--> trilogy (NOUN)`
9083	`Texas (PROPN) --[compound]--> Mathews (PROPN)`
9084	`Detective (PROPN) --[compound]--> Mathews (PROPN)`
9085	`Darren (PROPN) --[compound]--> Mathews (PROPN)`
9086	`Mathews (PROPN) --[dobj]--> starring (VERB)`
9087	`. (PUNCT) --[punct]--> is (AUX)`
9088
9089	`Sentence 3: It's that classic setup of, like, he's being pulled out of retirement to solve one more crime, you know what I mean?`
9090	`It (PRON) --[nsubj]--> 's (AUX)`
9091	`'s (AUX) --[ccomp]--> know (VERB)`
9092	`that (DET) --[det]--> setup (NOUN)`
9093	`classic (ADJ) --[amod]--> setup (NOUN)`
9094	`setup (NOUN) --[attr]--> 's (AUX)`
9095	`of (ADP) --[prep]--> setup (NOUN)`
9096	`, (PUNCT) --[punct]--> of (ADP)`
9097	`like (INTJ) --[intj]--> pulled (VERB)`
9098	`, (PUNCT) --[punct]--> pulled (VERB)`
9099	`he (PRON) --[nsubjpass]--> pulled (VERB)`
9100	`'s (AUX) --[aux]--> pulled (VERB)`
9101	`being (AUX) --[auxpass]--> pulled (VERB)`
9102	`pulled (VERB) --[advcl]--> 's (AUX)`
9103	`out (ADP) --[prep]--> pulled (VERB)`
9104	`of (ADP) --[prep]--> out (ADP)`
9105	`retirement (NOUN) --[pobj]--> of (ADP)`
9106	`to (PART) --[aux]--> solve (VERB)`
9107	`solve (VERB) --[advcl]--> pulled (VERB)`
9108	`one (NUM) --[nummod]--> crime (NOUN)`
9109	`more (ADJ) --[amod]--> one (NUM)`
9110	`crime (NOUN) --[dobj]--> solve (VERB)`
9111	`, (PUNCT) --[punct]--> know (VERB)`
9112	`you (PRON) --[nsubj]--> know (VERB)`
9113	`know (VERB) --[ROOT]--> know (VERB)`
9114	`what (PRON) --[dobj]--> mean (VERB)`
9115	`I (PRON) --[nsubj]--> mean (VERB)`
9116	`mean (VERB) --[ccomp]--> know (VERB)`
9117	`? (PUNCT) --[punct]--> know (VERB)`
9118
9119	`Total sentences: 3`
9120
9121	`RAKE Keyphrase Relationships:`
9122	`noun phrase: "Attica Locke's "Guide Me Home" contains [attica locke], [locke], [guide]`
9123	`noun phrase: "a longtime crime writer" contains [longtime crime writer], [crime]`
9124	`Total relationships found: 2`
9125
9126	`YAKE Keyphrase Relationships:`
9127	`noun phrase: "Attica Locke's "Guide Me Home" contains [guide me home], [attica locke], [guide], [home], [attica], [locke]`
9128	`noun phrase: "Texas Detective Darren Mathews" contains [detective darren mathews], [texas detective darren], [darren mathews], [texas detective], [detective darren], [mathews]`
9129	`Total relationships found: 2`
9130
9131	`Timing Comparison:`
9132	`RAKE: 0.0000sec`
9133	`YAKE: 0.0195sec`
9134	`KeyBERT: 0.0478sec`
9135	`Dependencies: 0.0130sec`
9136	`Fastest: RAKE`
9137
9138	`================================================================================`
9139	`Message 70:`
9140	`SHAPIRO: This character, much like you, is a swimmer, sang in show choir, was raised in Kansas. Did playing the role allow you to understand aspects of yourself that maybe you were only able to see once you sort of stepped outside of your daily reality and inhabited Sam?`
9141	`--------------------------------------------------------------------------------`
9142	`RAKE Keywords:`
9143	`- understand aspects (score: 4.00) → understand aspect`
9144	`- stepped outside (score: 4.00) → step outside`
9145	`- show choir (score: 4.00)`
9146	`- role allow (score: 4.00)`
9147	`- much like (score: 4.00)`
9148	`- inhabited sam (score: 4.00) → inhabit Sam`
9149	`- daily reality (score: 4.00)`
9150	`- swimmer (score: 1.00)`
9151	`- sort (score: 1.00)`
9152	`- shapiro (score: 1.00) → SHAPIRO`
9153	`- see (score: 1.00)`
9154	`- sang (score: 1.00) → sing`
9155	`- raised (score: 1.00) → raise`
9156	`- playing (score: 1.00) → play`
9157	`- maybe (score: 1.00)`
9158	`- kansas (score: 1.00) → Kansas`
9159	`- character (score: 1.00)`
9160	`- able (score: 1.00)`
9161	`Total keywords: 18 extracted in 0.0000 seconds`
9162
9163	`YAKE Keywords:`
9164	`- raised in Kansas (score: 0.01) → raise in Kansas`
9165	`- sang in show (score: 0.01) → sing in show`
9166	`- show choir (score: 0.01)`
9167	`- SHAPIRO (score: 0.04) → SHAPIRO`
9168	`- Kansas (score: 0.05) → Kansas`
9169	`- inhabited Sam (score: 0.05) → inhabit Sam`
9170	`- character (score: 0.09)`
9171	`- swimmer (score: 0.09)`
9172	`- sang (score: 0.09) → sing`
9173	`- choir (score: 0.09)`
9174	`- playing the role (score: 0.12) → play the role`
9175	`- understand aspects (score: 0.12) → understand aspect`
9176	`- sort of stepped (score: 0.12) → sort of step`
9177	`- daily reality (score: 0.12)`
9178	`- reality and inhabited (score: 0.12) → reality and inhabit`
9179	`- show (score: 0.12)`
9180	`- raised (score: 0.12) → raise`
9181	`- Sam (score: 0.16) → Sam`
9182	`- playing (score: 0.33) → play`
9183	`- role (score: 0.33)`
9184	`Total keywords: 20 extracted in 0.0152 seconds`
9185
9186	`KeyBERT Keywords:`
9187	`- shapiro character (score: 0.52)`
9188	`- shapiro character like (score: 0.51)`
9189	`- reality inhabited sam (score: 0.49)`
9190	`- inhabited sam (score: 0.46)`
9191	`- character like swimmer (score: 0.43)`
9192	`- shapiro (score: 0.42)`
9193	`- sam (score: 0.41)`
9194	`- choir raised kansas (score: 0.39)`
9195	`- sang choir raised (score: 0.38)`
9196	`- swimmer sang choir (score: 0.37)`
9197	`- choir raised (score: 0.37)`
9198	`- like swimmer sang (score: 0.36)`
9199	`- sang choir (score: 0.35)`
9200	`- did playing role (score: 0.35)`
9201	`- raised kansas did (score: 0.34)`
9202	`- swimmer sang (score: 0.33)`
9203	`- playing role (score: 0.32)`
9204	`- character (score: 0.31)`
9205	`- like swimmer (score: 0.31)`
9206	`- choir (score: 0.30)`
9207	`Total keywords: 20 extracted in 0.0416 seconds`
9208
9209	`Dependency Relations (extracted in 0.0080sec):`
9210
9211	`Sentence 1: SHAPIRO:`
9212	`SHAPIRO (PROPN) --[ROOT]--> SHAPIRO (PROPN)`
9213	`: (PUNCT) --[punct]--> SHAPIRO (PROPN)`
9214
9215	`Sentence 2: This character, much like you, is a swimmer, sang in show choir, was raised in Kansas.`
9216	`This (DET) --[det]--> character (NOUN)`
9217	`character (NOUN) --[nsubj]--> is (AUX)`
9218	`, (PUNCT) --[punct]--> is (AUX)`
9219	`much (ADV) --[advmod]--> like (ADP)`
9220	`like (ADP) --[prep]--> is (AUX)`
9221	`you (PRON) --[pobj]--> like (ADP)`
9222	`, (PUNCT) --[punct]--> is (AUX)`
9223	`is (AUX) --[ROOT]--> is (AUX)`
9224	`a (DET) --[det]--> swimmer (NOUN)`
9225	`swimmer (NOUN) --[attr]--> is (AUX)`
9226	`, (PUNCT) --[punct]--> is (AUX)`
9227	`sang (VERB) --[conj]--> is (AUX)`
9228	`in (ADP) --[prep]--> sang (VERB)`
9229	`show (NOUN) --[compound]--> choir (NOUN)`
9230	`choir (NOUN) --[pobj]--> in (ADP)`
9231	`, (PUNCT) --[punct]--> is (AUX)`
9232	`was (AUX) --[auxpass]--> raised (VERB)`
9233	`raised (VERB) --[conj]--> is (AUX)`
9234	`in (ADP) --[prep]--> raised (VERB)`
9235	`Kansas (PROPN) --[pobj]--> in (ADP)`
9236	`. (PUNCT) --[punct]--> is (AUX)`
9237
9238	`Sentence 3: Did playing the role allow you to understand aspects of yourself that maybe you were only able to see once you sort of stepped outside of your daily reality and inhabited Sam?`
9239	`Did (AUX) --[aux]--> playing (VERB)`
9240	`playing (VERB) --[csubj]--> allow (VERB)`
9241	`the (DET) --[det]--> role (NOUN)`
9242	`role (NOUN) --[dobj]--> playing (VERB)`
9243	`allow (VERB) --[ROOT]--> allow (VERB)`
9244	`you (PRON) --[nsubj]--> understand (VERB)`
9245	`to (PART) --[aux]--> understand (VERB)`
9246	`understand (VERB) --[ccomp]--> allow (VERB)`
9247	`aspects (NOUN) --[dobj]--> understand (VERB)`
9248	`of (ADP) --[prep]--> aspects (NOUN)`
9249	`yourself (PRON) --[pobj]--> of (ADP)`
9250	`that (SCONJ) --[mark]--> were (AUX)`
9251	`maybe (ADV) --[advmod]--> were (AUX)`
9252	`you (PRON) --[nsubj]--> were (AUX)`
9253	`were (AUX) --[ccomp]--> understand (VERB)`
9254	`only (ADV) --[advmod]--> were (AUX)`
9255	`able (ADJ) --[acomp]--> were (AUX)`
9256	`to (PART) --[aux]--> see (VERB)`
9257	`see (VERB) --[xcomp]--> able (ADJ)`
9258	`once (ADV) --[mark]--> stepped (VERB)`
9259	`you (PRON) --[nsubj]--> stepped (VERB)`
9260	`sort (ADV) --[advmod]--> of (ADV)`
9261	`of (ADV) --[advmod]--> stepped (VERB)`
9262	`stepped (VERB) --[ccomp]--> see (VERB)`
9263	`outside (ADV) --[advmod]--> stepped (VERB)`
9264	`of (ADP) --[prep]--> outside (ADV)`
9265	`your (PRON) --[poss]--> reality (NOUN)`
9266	`daily (ADJ) --[amod]--> reality (NOUN)`
9267	`reality (NOUN) --[pobj]--> of (ADP)`
9268	`and (CCONJ) --[cc]--> stepped (VERB)`
9269	`inhabited (VERB) --[conj]--> stepped (VERB)`
9270	`Sam (PROPN) --[dobj]--> inhabited (VERB)`
9271	`? (PUNCT) --[punct]--> allow (VERB)`
9272
9273	`Total sentences: 3`
9274
9275	`RAKE Keyphrase Relationships:`
9276	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
9277
9278	`YAKE Keyphrase Relationships:`
9279	`noun phrase: "show choir" contains [show choir], [choir], [show]`
9280	`verb phrase: "playing Did role" contains [playing], [role]`
9281	`verb phrase: "inhabited Sam" contains [inhabited sam], [sam]`
9282	`Total relationships found: 3`
9283
9284	`Timing Comparison:`
9285	`RAKE: 0.0000sec`
9286	`YAKE: 0.0152sec`
9287	`KeyBERT: 0.0416sec`
9288	`Dependencies: 0.0080sec`
9289	`Fastest: RAKE`
9290
9291	`================================================================================`
9292	`Message 71:`
9293	`ERIC WESTERVELT: In Diana and Lorrin Burdick's home in suburban Rancho Cordova, east of Sacramento, five parents are talking about their kids over a lunch of curried chicken, potato chips and fruit salad.UNIDENTIFIED PERSON #1: Her hair is finally growing out.UNIDENTIFIED PERSON #2: Oh.UNIDENTIFIED PERSON #3: Oh.UNIDENTIFIED PERSON #2: Yeah, that's better than the last time I saw her.`
9294	`--------------------------------------------------------------------------------`
9295	`RAKE Keywords:`
9296	`- suburban rancho cordova (score: 9.00) → suburban Rancho Cordova`
9297	`- unidentified person (score: 4.00) → UNIDENTIFIED person`
9298	`- unidentified person (score: 4.00) → UNIDENTIFIED person`
9299	`- unidentified person (score: 4.00) → UNIDENTIFIED person`
9300	`- unidentified person (score: 4.00) → UNIDENTIFIED person`
9301	`- potato chips (score: 4.00) → potato chip`
9302	`- lorrin burdick (score: 4.00) → Lorrin Burdick`
9303	`- last time (score: 4.00)`
9304	`- fruit salad (score: 4.00)`
9305	`- five parents (score: 4.00) → five parent`
9306	`- finally growing (score: 4.00) → finally grow`
9307	`- eric westervelt (score: 4.00) → ERIC WESTERVELT`
9308	`- curried chicken (score: 4.00) → curry chicken`
9309	`- yeah (score: 1.00)`
9310	`- talking (score: 1.00) → talk`
9311	`- saw (score: 1.00) → see`
9312	`- sacramento (score: 1.00) → Sacramento`
9313	`- oh (score: 1.00)`
9314	`- oh (score: 1.00)`
9315	`- lunch (score: 1.00)`
9316	`- kids (score: 1.00) → kid`
9317	`- home (score: 1.00)`
9318	`- hair (score: 1.00)`
9319	`- east (score: 1.00)`
9320	`- diana (score: 1.00) → Diana`
9321	`- better (score: 1.00) → well`
9322	`- 3 (score: 1.00)`
9323	`- 2 (score: 1.00)`
9324	`- 2 (score: 1.00)`
9325	`- 1 (score: 1.00)`
9326	`Total keywords: 30 extracted in 0.0000 seconds`
9327
9328	`YAKE Keywords:`
9329	`- suburban Rancho Cordova (score: 0.00) → suburban Rancho Cordova`
9330	`- Lorrin Burdick home (score: 0.00)`
9331	`- fruit salad.UNIDENTIFIED PERSON (score: 0.00)`
9332	`- growing out.UNIDENTIFIED PERSON (score: 0.00)`
9333	`- Oh.UNIDENTIFIED PERSON (score: 0.00)`
9334	`- finally growing out.UNIDENTIFIED (score: 0.00)`
9335	`- ERIC WESTERVELT (score: 0.00) → ERIC WESTERVELT`
9336	`- Rancho Cordova (score: 0.00) → Rancho Cordova`
9337	`- Diana and Lorrin (score: 0.00) → Diana and Lorrin`
9338	`- Lorrin Burdick (score: 0.00) → Lorrin Burdick`
9339	`- east of Sacramento (score: 0.00) → east of Sacramento`
9340	`- salad.UNIDENTIFIED PERSON (score: 0.01)`
9341	`- Burdick home (score: 0.01)`
9342	`- suburban Rancho (score: 0.01) → suburban Rancho`
9343	`- curried chicken (score: 0.01) → curry chicken`
9344	`- potato chips (score: 0.01) → potato chip`
9345	`- PERSON (score: 0.01)`
9346	`- home in suburban (score: 0.01)`
9347	`- parents are talking (score: 0.01) → parent be talk`
9348	`- lunch of curried (score: 0.01) → lunch of curry`
9349	`Total keywords: 20 extracted in 0.0418 seconds`
9350
9351	`KeyBERT Keywords:`
9352	`- unidentified person hair (score: 0.50)`
9353	`- diana lorrin burdick (score: 0.50)`
9354	`- salad unidentified person (score: 0.49)`
9355	`- growing unidentified person (score: 0.46)`
9356	`- east sacramento parents (score: 0.46)`
9357	`- lorrin burdick home (score: 0.43)`
9358	`- sacramento parents talking (score: 0.43)`
9359	`- salad unidentified (score: 0.43)`
9360	`- person hair finally (score: 0.43)`
9361	`- sacramento parents (score: 0.42)`
9362	`- parents talking kids (score: 0.42)`
9363	`- lorrin burdick (score: 0.42)`
9364	`- diana lorrin (score: 0.42)`
9365	`- eric westervelt diana (score: 0.42)`
9366	`- parents talking (score: 0.42)`
9367	`- talking kids lunch (score: 0.41)`
9368	`- westervelt diana lorrin (score: 0.41)`
9369	`- hair finally growing (score: 0.41)`
9370	`- burdick home (score: 0.39)`
9371	`- unidentified person oh (score: 0.39)`
9372	`Total keywords: 20 extracted in 0.0674 seconds`
9373
9374	`Dependency Relations (extracted in 0.0140sec):`
9375
9376	`Sentence 1: ERIC WESTERVELT:`
9377	`ERIC (PROPN) --[compound]--> WESTERVELT (PROPN)`
9378	`WESTERVELT (PROPN) --[ROOT]--> WESTERVELT (PROPN)`
9379	`: (PUNCT) --[punct]--> WESTERVELT (PROPN)`
9380
9381	`Sentence 2: In Diana and Lorrin Burdick's home in suburban Rancho Cordova, east of Sacramento, five parents are talking about their kids over a lunch of curried chicken, potato chips and fruit salad.`
9382	`In (ADP) --[prep]--> talking (VERB)`
9383	`Diana (PROPN) --[pobj]--> In (ADP)`
9384	`and (CCONJ) --[cc]--> Diana (PROPN)`
9385	`Lorrin (PROPN) --[compound]--> Burdick (PROPN)`
9386	`Burdick (PROPN) --[conj]--> Diana (PROPN)`
9387	`'s (PART) --[case]--> Burdick (PROPN)`
9388	`home (NOUN) --[pobj]--> In (ADP)`
9389	`in (ADP) --[prep]--> home (NOUN)`
9390	`suburban (ADJ) --[amod]--> Cordova (PROPN)`
9391	`Rancho (PROPN) --[compound]--> Cordova (PROPN)`
9392	`Cordova (PROPN) --[pobj]--> in (ADP)`
9393	`, (PUNCT) --[punct]--> Cordova (PROPN)`
9394	`east (ADV) --[pobj]--> In (ADP)`
9395	`of (ADP) --[prep]--> east (ADV)`
9396	`Sacramento (PROPN) --[pobj]--> of (ADP)`
9397	`, (PUNCT) --[punct]--> talking (VERB)`
9398	`five (NUM) --[nummod]--> parents (NOUN)`
9399	`parents (NOUN) --[nsubj]--> talking (VERB)`
9400	`are (AUX) --[aux]--> talking (VERB)`
9401	`talking (VERB) --[ROOT]--> talking (VERB)`
9402	`about (ADP) --[prep]--> talking (VERB)`
9403	`their (PRON) --[poss]--> kids (NOUN)`
9404	`kids (NOUN) --[pobj]--> about (ADP)`
9405	`over (ADP) --[prep]--> talking (VERB)`
9406	`a (DET) --[det]--> lunch (NOUN)`
9407	`lunch (NOUN) --[pobj]--> over (ADP)`
9408	`of (ADP) --[prep]--> lunch (NOUN)`
9409	`curried (VERB) --[amod]--> chicken (NOUN)`
9410	`chicken (NOUN) --[pobj]--> of (ADP)`
9411	`, (PUNCT) --[punct]--> chicken (NOUN)`
9412	`potato (NOUN) --[compound]--> chips (NOUN)`
9413	`chips (NOUN) --[conj]--> chicken (NOUN)`
9414	`and (CCONJ) --[cc]--> chips (NOUN)`
9415	`fruit (NOUN) --[compound]--> salad (NOUN)`
9416	`salad (NOUN) --[conj]--> chips (NOUN)`
9417	`. (PUNCT) --[punct]--> talking (VERB)`
9418
9419	`Sentence 3: UNIDENTIFIED PERSON #1: Her hair is finally growing out.`
9420	`UNIDENTIFIED (PROPN) --[compound]--> PERSON (NOUN)`
9421	`PERSON (NOUN) --[npadvmod]--> growing (VERB)`
9422	`# (SYM) --[nmod]--> 1 (NUM)`
9423	`1 (NUM) --[nummod]--> PERSON (NOUN)`
9424	`: (PUNCT) --[punct]--> growing (VERB)`
9425	`Her (PRON) --[poss]--> hair (NOUN)`
9426	`hair (NOUN) --[nsubj]--> growing (VERB)`
9427	`is (AUX) --[aux]--> growing (VERB)`
9428	`finally (ADV) --[advmod]--> growing (VERB)`
9429	`growing (VERB) --[ROOT]--> growing (VERB)`
9430	`out (ADP) --[prt]--> growing (VERB)`
9431	`. (PUNCT) --[punct]--> growing (VERB)`
9432
9433	`Sentence 4: UNIDENTIFIED PERSON #2: Oh.UNIDENTIFIED PERSON #3: Oh.UNIDENTIFIED PERSON #2:`
9434	`UNIDENTIFIED (PROPN) --[compound]--> PERSON (NOUN)`
9435	`PERSON (NOUN) --[ROOT]--> PERSON (NOUN)`
9436	`# (SYM) --[nmod]--> 2 (NUM)`
9437	`2 (NUM) --[appos]--> PERSON (NOUN)`
9438	`: (PUNCT) --[punct]--> PERSON (NOUN)`
9439	`Oh (INTJ) --[intj]--> PERSON (NOUN)`
9440	`. (PUNCT) --[punct]--> Oh (INTJ)`
9441	`UNIDENTIFIED (PROPN) --[appos]--> PERSON (NOUN)`
9442	`PERSON (NOUN) --[appos]--> PERSON (NOUN)`
9443	`# (SYM) --[nmod]--> 3 (NUM)`
9444	`3 (NUM) --[appos]--> PERSON (NOUN)`
9445	`: (PUNCT) --[punct]--> PERSON (NOUN)`
9446	`Oh (INTJ) --[intj]--> PERSON (NOUN)`
9447	`. (PUNCT) --[punct]--> Oh (INTJ)`
9448	`UNIDENTIFIED (PROPN) --[appos]--> PERSON (NOUN)`
9449	`PERSON (NOUN) --[appos]--> PERSON (NOUN)`
9450	`# (SYM) --[nmod]--> 2 (NUM)`
9451	`2 (NUM) --[appos]--> PERSON (NOUN)`
9452	`: (PUNCT) --[punct]--> PERSON (NOUN)`
9453
9454	`Sentence 5: Yeah, that's better than the last time I saw her.`
9455	`Yeah (INTJ) --[intj]--> 's (AUX)`
9456	`, (PUNCT) --[punct]--> 's (AUX)`
9457	`that (PRON) --[nsubj]--> 's (AUX)`
9458	`'s (AUX) --[ROOT]--> 's (AUX)`
9459	`better (ADJ) --[acomp]--> 's (AUX)`
9460	`than (ADP) --[prep]--> better (ADJ)`
9461	`the (DET) --[det]--> time (NOUN)`
9462	`last (ADJ) --[amod]--> time (NOUN)`
9463	`time (NOUN) --[pobj]--> than (ADP)`
9464	`I (PRON) --[nsubj]--> saw (VERB)`
9465	`saw (VERB) --[relcl]--> time (NOUN)`
9466	`her (PRON) --[dobj]--> saw (VERB)`
9467	`. (PUNCT) --[punct]--> 's (AUX)`
9468
9469	`Total sentences: 5`
9470
9471	`RAKE Keyphrase Relationships:`
9472	`noun phrase: "UNIDENTIFIED PERSON" contains [unidentified person], [unidentified person], [unidentified person], [unidentified person]`
9473	`Total relationships found: 1`
9474
9475	`YAKE Keyphrase Relationships:`
9476	`noun phrase: "suburban Rancho Cordova" contains [suburban rancho cordova], [rancho cordova], [suburban rancho]`
9477	`Total relationships found: 1`
9478
9479	`Timing Comparison:`
9480	`RAKE: 0.0000sec`
9481	`YAKE: 0.0418sec`
9482	`KeyBERT: 0.0674sec`
9483	`Dependencies: 0.0140sec`
9484	`Fastest: RAKE`
9485
9486	`================================================================================`
9487	`Message 72:`
9488	BONDY: Well, I think Canada was clear all along that if it didn't agree with what was on the table, if it was not the right deal, that it was not going to sign at any cost. It seems to me that this date of Friday was something that was manufactured by the U.S. administration in an attempt to meet the deadlines for congressional approval and in relation to the Mexican president's stepping down and being replaced and so kind of trying to push a deal through. Canada said all along what matters is getting the right deal and that if we don't have that on the table, we'll keep talking. That's in effect what's happened.
9489	`--------------------------------------------------------------------------------`
9490	`RAKE Keywords:`
9491	`- think canada (score: 4.00) → think Canada`
9492	`- mexican president (score: 4.00)`
9493	`- keep talking (score: 4.00) → keep talk`
9494	`- congressional approval (score: 4.00)`
9495	`- canada said (score: 4.00) → Canada say`
9496	`- right deal (score: 3.67)`
9497	`- right deal (score: 3.67)`
9498	`- deal (score: 1.67)`
9499	`- well (score: 1.00)`
9500	`- u (score: 1.00)`
9501	`- trying (score: 1.00) → try`
9502	`- table (score: 1.00)`
9503	`- table (score: 1.00)`
9504	`- stepping (score: 1.00) → step`
9505	`- something (score: 1.00)`
9506	`- sign (score: 1.00)`
9507	`- seems (score: 1.00) → seem`
9508	`- replaced (score: 1.00) → replace`
9509	`- relation (score: 1.00)`
9510	`- push (score: 1.00)`
9511	`- meet (score: 1.00)`
9512	`- matters (score: 1.00) → matter`
9513	`- manufactured (score: 1.00) → manufacture`
9514	`- kind (score: 1.00)`
9515	`- happened (score: 1.00) → happen`
9516	`- going (score: 1.00) → go`
9517	`- getting (score: 1.00) → get`
9518	`- friday (score: 1.00) → Friday`
9519	`- effect (score: 1.00)`
9520	`- deadlines (score: 1.00) → deadline`
9521	`- date (score: 1.00)`
9522	`- cost (score: 1.00)`
9523	`- clear (score: 1.00)`
9524	`- bondy (score: 1.00) → BONDY`
9525	`- attempt (score: 1.00)`
9526	`- along (score: 1.00)`
9527	`- along (score: 1.00)`
9528	`- agree (score: 1.00)`
9529	`- administration (score: 1.00)`
9530	`Total keywords: 39 extracted in 0.0000 seconds`
9531
9532	`YAKE Keywords:`
9533	`- Mexican president stepping (score: 0.04)`
9534	`- BONDY (score: 0.05) → BONDY`
9535	`- Canada was clear (score: 0.08) → Canada be clear`
9536	`- date of Friday (score: 0.08) → date of Friday`
9537	`- Mexican president (score: 0.08)`
9538	`- deal (score: 0.09)`
9539	`- table (score: 0.10)`
9540	`- Canada (score: 0.12) → Canada`
9541	`- cost (score: 0.12)`
9542	`- clear (score: 0.15)`
9543	`- agree (score: 0.15)`
9544	`- sign (score: 0.15)`
9545	`- attempt to meet (score: 0.17)`
9546	`- meet the deadlines (score: 0.17) → meet the deadline`
9547	`- deadlines for congressional (score: 0.17) → deadline for congressional`
9548	`- congressional approval (score: 0.17)`
9549	`- president stepping (score: 0.17)`
9550	`- Friday (score: 0.20) → Friday`
9551	`- Mexican (score: 0.20)`
9552	`- push a deal (score: 0.28)`
9553	`Total keywords: 20 extracted in 0.0232 seconds`
9554
9555	`KeyBERT Keywords:`
9556	`- deal canada said (score: 0.62)`
9557	`- deal canada (score: 0.56)`
9558	`- push deal canada (score: 0.50)`
9559	`- deal going sign (score: 0.43)`
9560	`- canada said matters (score: 0.41)`
9561	`- canada said (score: 0.41)`
9562	`- sign cost date (score: 0.40)`
9563	`- cost date friday (score: 0.40)`
9564	`- canada clear didn (score: 0.40)`
9565	`- bondy think canada (score: 0.38)`
9566	`- date friday (score: 0.37)`
9567	`- right deal going (score: 0.36)`
9568	`- date friday manufactured (score: 0.36)`
9569	`- deal don table (score: 0.34)`
9570	`- deal going (score: 0.32)`
9571	`- cost date (score: 0.32)`
9572	`- getting right deal (score: 0.31)`
9573	`- right deal don (score: 0.31)`
9574	`- going sign cost (score: 0.31)`
9575	`- canada clear (score: 0.30)`
9576	`Total keywords: 20 extracted in 0.0677 seconds`
9577
9578	`Dependency Relations (extracted in 0.0215sec):`
9579
9580	`Sentence 1: BONDY:`
9581	`BONDY (PROPN) --[ROOT]--> BONDY (PROPN)`
9582	`: (PUNCT) --[punct]--> BONDY (PROPN)`
9583
9584	`Sentence 2: Well, I think Canada was clear all along that if it didn't agree with what was on the table, if it was not the right deal, that it was not going to sign at any cost.`
9585	`Well (INTJ) --[intj]--> think (VERB)`
9586	`, (PUNCT) --[punct]--> think (VERB)`
9587	`I (PRON) --[nsubj]--> think (VERB)`
9588	`think (VERB) --[ROOT]--> think (VERB)`
9589	`Canada (PROPN) --[nsubj]--> was (AUX)`
9590	`was (AUX) --[ccomp]--> think (VERB)`
9591	`clear (ADJ) --[acomp]--> was (AUX)`
9592	`all (ADV) --[advmod]--> along (ADV)`
9593	`along (ADV) --[advmod]--> was (AUX)`
9594	`that (SCONJ) --[mark]--> going (VERB)`
9595	`if (SCONJ) --[mark]--> agree (VERB)`
9596	`it (PRON) --[nsubj]--> agree (VERB)`
9597	`did (AUX) --[aux]--> agree (VERB)`
9598	`n't (PART) --[neg]--> agree (VERB)`
9599	`agree (VERB) --[advcl]--> going (VERB)`
9600	`with (ADP) --[prep]--> agree (VERB)`
9601	`what (PRON) --[nsubj]--> was (AUX)`
9602	`was (AUX) --[pcomp]--> with (ADP)`
9603	`on (ADP) --[prep]--> was (AUX)`
9604	`the (DET) --[det]--> table (NOUN)`
9605	`table (NOUN) --[pobj]--> on (ADP)`
9606	`, (PUNCT) --[punct]--> going (VERB)`
9607	`if (SCONJ) --[mark]--> was (AUX)`
9608	`it (PRON) --[nsubj]--> was (AUX)`
9609	`was (AUX) --[advcl]--> going (VERB)`
9610	`not (PART) --[neg]--> was (AUX)`
9611	`the (DET) --[det]--> deal (NOUN)`
9612	`right (ADJ) --[amod]--> deal (NOUN)`
9613	`deal (NOUN) --[attr]--> was (AUX)`
9614	`, (PUNCT) --[punct]--> going (VERB)`
9615	`that (SCONJ) --[mark]--> going (VERB)`
9616	`it (PRON) --[nsubj]--> going (VERB)`
9617	`was (AUX) --[aux]--> going (VERB)`
9618	`not (PART) --[neg]--> going (VERB)`
9619	`going (VERB) --[advcl]--> was (AUX)`
9620	`to (PART) --[aux]--> sign (VERB)`
9621	`sign (VERB) --[xcomp]--> going (VERB)`
9622	`at (ADP) --[prep]--> sign (VERB)`
9623	`any (DET) --[det]--> cost (NOUN)`
9624	`cost (NOUN) --[pobj]--> at (ADP)`
9625	`. (PUNCT) --[punct]--> think (VERB)`
9626
9627	`Sentence 3: It seems to me that this date of Friday was something that was manufactured by the U.S. administration in an attempt to meet the deadlines for congressional approval and in relation to the Mexican president's stepping down and being replaced and so kind of trying to push a deal through.`
9628	`It (PRON) --[nsubj]--> seems (VERB)`
9629	`seems (VERB) --[ROOT]--> seems (VERB)`
9630	`to (ADP) --[prep]--> seems (VERB)`
9631	`me (PRON) --[pobj]--> to (ADP)`
9632	`that (SCONJ) --[mark]--> was (AUX)`
9633	`this (DET) --[det]--> date (NOUN)`
9634	`date (NOUN) --[nsubj]--> was (AUX)`
9635	`of (ADP) --[prep]--> date (NOUN)`
9636	`Friday (PROPN) --[pobj]--> of (ADP)`
9637	`was (AUX) --[ccomp]--> seems (VERB)`
9638	`something (PRON) --[attr]--> was (AUX)`
9639	`that (PRON) --[nsubjpass]--> manufactured (VERB)`
9640	`was (AUX) --[auxpass]--> manufactured (VERB)`
9641	`manufactured (VERB) --[relcl]--> something (PRON)`
9642	`by (ADP) --[agent]--> manufactured (VERB)`
9643	`the (DET) --[det]--> administration (NOUN)`
9644	`U.S. (PROPN) --[compound]--> administration (NOUN)`
9645	`administration (NOUN) --[pobj]--> by (ADP)`
9646	`in (ADP) --[prep]--> manufactured (VERB)`
9647	`an (DET) --[det]--> attempt (NOUN)`
9648	`attempt (NOUN) --[pobj]--> in (ADP)`
9649	`to (PART) --[aux]--> meet (VERB)`
9650	`meet (VERB) --[acl]--> attempt (NOUN)`
9651	`the (DET) --[det]--> deadlines (NOUN)`
9652	`deadlines (NOUN) --[dobj]--> meet (VERB)`
9653	`for (ADP) --[prep]--> meet (VERB)`
9654	`congressional (ADJ) --[amod]--> approval (NOUN)`
9655	`approval (NOUN) --[pobj]--> for (ADP)`
9656	`and (CCONJ) --[cc]--> for (ADP)`
9657	`in (ADP) --[prep]--> stepping (VERB)`
9658	`relation (NOUN) --[pobj]--> in (ADP)`
9659	`to (ADP) --[prep]--> relation (NOUN)`
9660	`the (DET) --[det]--> president (NOUN)`
9661	`Mexican (ADJ) --[amod]--> president (NOUN)`
9662	`president (NOUN) --[pobj]--> to (ADP)`
9663	`'s (PART) --[case]--> president (NOUN)`
9664	`stepping (VERB) --[conj]--> manufactured (VERB)`
9665	`down (ADP) --[prt]--> stepping (VERB)`
9666	`and (CCONJ) --[cc]--> stepping (VERB)`
9667	`being (AUX) --[auxpass]--> replaced (VERB)`
9668	`replaced (VERB) --[conj]--> stepping (VERB)`
9669	`and (CCONJ) --[cc]--> replaced (VERB)`
9670	`so (ADV) --[advmod]--> trying (VERB)`
9671	`kind (ADV) --[advmod]--> of (ADV)`
9672	`of (ADV) --[advmod]--> trying (VERB)`
9673	`trying (VERB) --[conj]--> replaced (VERB)`
9674	`to (PART) --[aux]--> push (VERB)`
9675	`push (VERB) --[xcomp]--> trying (VERB)`
9676	`a (DET) --[det]--> deal (NOUN)`
9677	`deal (NOUN) --[dobj]--> push (VERB)`
9678	`through (ADV) --[advmod]--> push (VERB)`
9679	`. (PUNCT) --[punct]--> seems (VERB)`
9680
9681	`Sentence 4: Canada said all along what matters is getting the right deal and that if we don't have that on the table, we'll keep talking.`
9682	`Canada (PROPN) --[nsubj]--> said (VERB)`
9683	`said (VERB) --[ROOT]--> said (VERB)`
9684	`all (ADV) --[advmod]--> along (ADP)`
9685	`along (ADP) --[prep]--> said (VERB)`
9686	`what (PRON) --[nsubj]--> matters (VERB)`
9687	`matters (VERB) --[pcomp]--> along (ADP)`
9688	`is (AUX) --[aux]--> getting (VERB)`
9689	`getting (VERB) --[ccomp]--> said (VERB)`
9690	`the (DET) --[det]--> deal (NOUN)`
9691	`right (ADJ) --[amod]--> deal (NOUN)`
9692	`deal (NOUN) --[dobj]--> getting (VERB)`
9693	`and (CCONJ) --[cc]--> getting (VERB)`
9694	`that (SCONJ) --[mark]--> keep (VERB)`
9695	`if (SCONJ) --[mark]--> have (VERB)`
9696	`we (PRON) --[nsubj]--> have (VERB)`
9697	`do (AUX) --[aux]--> have (VERB)`
9698	`n't (PART) --[neg]--> have (VERB)`
9699	`have (VERB) --[advcl]--> keep (VERB)`
9700	`that (PRON) --[dobj]--> have (VERB)`
9701	`on (ADP) --[prep]--> have (VERB)`
9702	`the (DET) --[det]--> table (NOUN)`
9703	`table (NOUN) --[pobj]--> on (ADP)`
9704	`, (PUNCT) --[punct]--> keep (VERB)`
9705	`we (PRON) --[nsubj]--> keep (VERB)`
9706	`'ll (AUX) --[aux]--> keep (VERB)`
9707	`keep (VERB) --[conj]--> getting (VERB)`
9708	`talking (VERB) --[xcomp]--> keep (VERB)`
9709	`. (PUNCT) --[punct]--> said (VERB)`
9710
9711	`Sentence 5: That's in effect what's happened.`
9712	`That (PRON) --[nsubj]--> 's (AUX)`
9713	`'s (AUX) --[ROOT]--> 's (AUX)`
9714	`in (ADP) --[prep]--> 's (AUX)`
9715	`effect (NOUN) --[pobj]--> in (ADP)`
9716	`what (PRON) --[nsubjpass]--> happened (VERB)`
9717	`'s (AUX) --[auxpass]--> happened (VERB)`
9718	`happened (VERB) --[ccomp]--> 's (AUX)`
9719	`. (PUNCT) --[punct]--> 's (AUX)`
9720
9721	`Total sentences: 5`
9722
9723	`RAKE Keyphrase Relationships:`
9724	`noun phrase: "the table" contains [table], [table]`
9725	`noun phrase: "the right deal" contains [right deal], [right deal], [deal]`
9726	`noun phrase: "the U.S. administration" contains [u], [administration]`
9727	`noun phrase: "the right deal" contains [right deal], [right deal], [deal]`
9728	`noun phrase: "the table" contains [table], [table]`
9729	`verb phrase: "manufactured was in" contains [u], [manufactured]`
9730	`verb phrase: "meet to deadlines for" contains [meet], [deadlines]`
9731	`verb phrase: "push to deal through" contains [deal], [u], [push]`
9732	`verb phrase: "said along" contains [along], [along]`
9733	`verb phrase: "getting is deal" contains [deal], [getting]`
9734	`Total relationships found: 10`
9735
9736	`YAKE Keyphrase Relationships:`
9737	`noun phrase: "the Mexican president" contains [mexican president], [mexican]`
9738	`Total relationships found: 1`
9739
9740	`Timing Comparison:`
9741	`RAKE: 0.0000sec`
9742	`YAKE: 0.0232sec`
9743	`KeyBERT: 0.0677sec`
9744	`Dependencies: 0.0215sec`
9745	`Fastest: RAKE`
9746
9747	`================================================================================`
9748	`Message 73:`
9749	BODETTE: So that's incredibly meaningful because, you know, again, women are far from the only oppressed group under the current Iranian state, but they're the first and the largest one. And the dress codes that these women are protesting are just the tip of the iceberg. Iranian law grants men, government an incredible degree of power and control over really the most intimate and personal aspect of women's lives, from marriage and divorce and inheritance to travel, work and legal cases.And notably, this is not the first time that Iranian women have courageously resisted these discriminatory practices. But it is the first time that these feminist demands, these demands for the basic equality of women, have sparked such a sustained, nationwide protest against dictatorship as a whole. Because all women in Iran face this severe oppression, their demands unify people across these religious and ethnic lines. And because gender inequality is so foundational to the state and its repressive institutions, a demand for women's freedom is naturally, I would say, a demand for the democracy and freedom from all the kinds of human rights abuses that women and men alike suffer from there.
9750	`--------------------------------------------------------------------------------`
9751	`RAKE Keywords:`
9752	`- men alike suffer (score: 9.00) → man alike suffer`
9753	`- human rights abuses (score: 9.00) → human right abuse`
9754	`- current iranian state (score: 7.50)`
9755	`- would say (score: 4.00)`
9756	`- severe oppression (score: 4.00)`
9757	`- repressive institutions (score: 4.00) → repressive institution`
9758	`- personal aspect (score: 4.00)`
9759	`- oppressed group (score: 4.00) → oppress group`
9760	`- nationwide protest (score: 4.00)`
9761	`- legal cases (score: 4.00) → legal case`
9762	`- largest one (score: 4.00) → large one`
9763	`- iran face (score: 4.00) → Iran face`
9764	`- incredibly meaningful (score: 4.00)`
9765	`- incredible degree (score: 4.00)`
9766	`- gender inequality (score: 4.00)`
9767	`- ethnic lines (score: 4.00) → ethnic line`
9768	`- dress codes (score: 4.00) → dress code`
9769	`- discriminatory practices (score: 4.00) → discriminatory practice`
9770	`- courageously resisted (score: 4.00) → courageously resist`
9771	`- basic equality (score: 4.00)`
9772	`- first time (score: 3.67)`
9773	`- first time (score: 3.67)`
9774	`- iranian women (score: 3.62) → iranian woman`
9775	`- feminist demands (score: 3.50) → feminist demand`
9776	`- state (score: 2.00)`
9777	`- first (score: 1.67)`
9778	`- demands (score: 1.50) → demand`
9779	`- women (score: 1.12) → woman`
9780	`- women (score: 1.12) → woman`
9781	`- women (score: 1.12) → woman`
9782	`- women (score: 1.12) → woman`
9783	`- women (score: 1.12) → woman`
9784	`- women (score: 1.12) → woman`
9785	`- women (score: 1.12) → woman`
9786	`- work (score: 1.00)`
9787	`- whole (score: 1.00)`
9788	`- travel (score: 1.00)`
9789	`- tip (score: 1.00)`
9790	`- sustained (score: 1.00)`
9791	`- sparked (score: 1.00) → spark`
9792	`- religious (score: 1.00)`
9793	`- really (score: 1.00)`
9794	`- protesting (score: 1.00) → protest`
9795	`- power (score: 1.00)`
9796	`- notably (score: 1.00)`
9797	`- naturally (score: 1.00)`
9798	`- marriage (score: 1.00)`
9799	`- lives (score: 1.00) → life`
9800	`- know (score: 1.00)`
9801	`- kinds (score: 1.00) → kind`
9802	`- intimate (score: 1.00)`
9803	`- inheritance (score: 1.00)`
9804	`- iceberg (score: 1.00)`
9805	`- government (score: 1.00)`
9806	`- freedom (score: 1.00)`
9807	`- freedom (score: 1.00)`
9808	`- foundational (score: 1.00)`
9809	`- far (score: 1.00)`
9810	`- divorce (score: 1.00)`
9811	`- dictatorship (score: 1.00)`
9812	`- democracy (score: 1.00)`
9813	`- demand (score: 1.00)`
9814	`- demand (score: 1.00)`
9815	`- control (score: 1.00)`
9816	`- bodette (score: 1.00) → BODETTE`
9817	`Total keywords: 65 extracted in 0.0017 seconds`
9818
9819	`YAKE Keywords:`
9820	`- current Iranian state (score: 0.01)`
9821	`- incredibly meaningful (score: 0.03)`
9822	`- oppressed group (score: 0.03) → oppress group`
9823	`- current Iranian (score: 0.03)`
9824	`- women (score: 0.04) → woman`
9825	`- BODETTE (score: 0.05) → BODETTE`
9826	`- Iranian women (score: 0.06) → iranian woman`
9827	`- Iranian (score: 0.06)`
9828	`- Iranian law grants (score: 0.07) → iranian law grant`
9829	`- Iranian state (score: 0.08)`
9830	`- Iranian law (score: 0.10)`
9831	`- demands (score: 0.11) → demand`
9832	`- incredibly (score: 0.17)`
9833	`- meaningful (score: 0.17)`
9834	`- oppressed (score: 0.17) → oppress`
9835	`- group (score: 0.17)`
9836	`- current (score: 0.17)`
9837	`- largest (score: 0.17) → large`
9838	`- demand (score: 0.17)`
9839	`- women lives (score: 0.17)`
9840	`Total keywords: 20 extracted in 0.0252 seconds`
9841
9842	`KeyBERT Keywords:`
9843	`- women iran (score: 0.71)`
9844	`- iranian women courageously (score: 0.69)`
9845	`- dictatorship women iran (score: 0.68)`
9846	`- iranian women (score: 0.67)`
9847	`- women iran face (score: 0.67)`
9848	`- time iranian women (score: 0.63)`
9849	`- demand women freedom (score: 0.58)`
9850	`- iranian law (score: 0.58)`
9851	`- feminist demands (score: 0.57)`
9852	`- feminist demands demands (score: 0.56)`
9853	`- protest dictatorship women (score: 0.53)`
9854	`- current iranian state (score: 0.52)`
9855	`- women freedom (score: 0.49)`
9856	`- women far oppressed (score: 0.49)`
9857	`- oppression demands (score: 0.48)`
9858	`- iranian state (score: 0.48)`
9859	`- oppression demands unify (score: 0.47)`
9860	`- women freedom naturally (score: 0.47)`
9861	`- demand women (score: 0.46)`
9862	`- equality women (score: 0.46)`
9863	`Total keywords: 20 extracted in 0.1424 seconds`
9864
9865	`Dependency Relations (extracted in 0.0362sec):`
9866
9867	`Sentence 1: BODETTE:`
9868	`BODETTE (NOUN) --[ROOT]--> BODETTE (NOUN)`
9869	`: (PUNCT) --[punct]--> BODETTE (NOUN)`
9870
9871	`Sentence 2: So that's incredibly meaningful because, you know, again, women are far from the only oppressed group under the current Iranian state, but they're the first and the largest one.`
9872	`So (ADV) --[advmod]--> 's (AUX)`
9873	`that (PRON) --[nsubj]--> 's (AUX)`
9874	`'s (AUX) --[ROOT]--> 's (AUX)`
9875	`incredibly (ADV) --[advmod]--> meaningful (ADJ)`
9876	`meaningful (ADJ) --[acomp]--> 's (AUX)`
9877	`because (SCONJ) --[mark]--> are (AUX)`
9878	`, (PUNCT) --[punct]--> are (AUX)`
9879	`you (PRON) --[nsubj]--> know (VERB)`
9880	`know (VERB) --[parataxis]--> are (AUX)`
9881	`, (PUNCT) --[punct]--> are (AUX)`
9882	`again (ADV) --[advmod]--> are (AUX)`
9883	`, (PUNCT) --[punct]--> are (AUX)`
9884	`women (NOUN) --[nsubj]--> are (AUX)`
9885	`are (AUX) --[advcl]--> 's (AUX)`
9886	`far (ADV) --[advmod]--> are (AUX)`
9887	`from (ADP) --[prep]--> far (ADV)`
9888	`the (DET) --[det]--> group (NOUN)`
9889	`only (ADJ) --[amod]--> group (NOUN)`
9890	`oppressed (VERB) --[amod]--> group (NOUN)`
9891	`group (NOUN) --[pobj]--> from (ADP)`
9892	`under (ADP) --[prep]--> group (NOUN)`
9893	`the (DET) --[det]--> state (NOUN)`
9894	`current (ADJ) --[amod]--> state (NOUN)`
9895	`Iranian (ADJ) --[amod]--> state (NOUN)`
9896	`state (NOUN) --[pobj]--> under (ADP)`
9897	`, (PUNCT) --[punct]--> 's (AUX)`
9898	`but (CCONJ) --[cc]--> 's (AUX)`
9899	`they (PRON) --[nsubj]--> 're (AUX)`
9900	`'re (AUX) --[conj]--> 's (AUX)`
9901	`the (DET) --[det]--> first (ADJ)`
9902	`first (ADJ) --[attr]--> 're (AUX)`
9903	`and (CCONJ) --[cc]--> first (ADJ)`
9904	`the (DET) --[det]--> one (NUM)`
9905	`largest (ADJ) --[amod]--> one (NUM)`
9906	`one (NUM) --[conj]--> first (ADJ)`
9907	`. (PUNCT) --[punct]--> 're (AUX)`
9908
9909	`Sentence 3: And the dress codes that these women are protesting are just the tip of the iceberg.`
9910	`And (CCONJ) --[cc]--> codes (NOUN)`
9911	`the (DET) --[det]--> codes (NOUN)`
9912	`dress (NOUN) --[compound]--> codes (NOUN)`
9913	`codes (NOUN) --[nsubj]--> are (AUX)`
9914	`that (SCONJ) --[dobj]--> protesting (VERB)`
9915	`these (DET) --[det]--> women (NOUN)`
9916	`women (NOUN) --[nsubj]--> protesting (VERB)`
9917	`are (AUX) --[aux]--> protesting (VERB)`
9918	`protesting (VERB) --[acl]--> codes (NOUN)`
9919	`are (AUX) --[ROOT]--> are (AUX)`
9920	`just (ADV) --[advmod]--> tip (NOUN)`
9921	`the (DET) --[det]--> tip (NOUN)`
9922	`tip (NOUN) --[attr]--> are (AUX)`
9923	`of (ADP) --[prep]--> tip (NOUN)`
9924	`the (DET) --[det]--> iceberg (NOUN)`
9925	`iceberg (NOUN) --[pobj]--> of (ADP)`
9926	`. (PUNCT) --[punct]--> are (AUX)`
9927
9928	`Sentence 4: Iranian law grants men, government an incredible degree of power and control over really the most intimate and personal aspect of women's lives, from marriage and divorce and inheritance to travel, work and legal cases.`
9929	`Iranian (ADJ) --[amod]--> law (NOUN)`
9930	`law (NOUN) --[nsubj]--> grants (VERB)`
9931	`grants (VERB) --[ROOT]--> grants (VERB)`
9932	`men (NOUN) --[dobj]--> grants (VERB)`
9933	`, (PUNCT) --[punct]--> grants (VERB)`
9934	`government (NOUN) --[conj]--> grants (VERB)`
9935	`an (DET) --[det]--> degree (NOUN)`
9936	`incredible (ADJ) --[amod]--> degree (NOUN)`
9937	`degree (NOUN) --[dobj]--> government (NOUN)`
9938	`of (ADP) --[prep]--> degree (NOUN)`
9939	`power (NOUN) --[pobj]--> of (ADP)`
9940	`and (CCONJ) --[cc]--> power (NOUN)`
9941	`control (NOUN) --[conj]--> power (NOUN)`
9942	`over (ADP) --[prep]--> government (NOUN)`
9943	`really (ADV) --[advmod]--> aspect (NOUN)`
9944	`the (DET) --[det]--> aspect (NOUN)`
9945	`most (ADV) --[advmod]--> intimate (ADJ)`
9946	`intimate (ADJ) --[amod]--> aspect (NOUN)`
9947	`and (CCONJ) --[cc]--> intimate (ADJ)`
9948	`personal (ADJ) --[conj]--> intimate (ADJ)`
9949	`aspect (NOUN) --[pobj]--> over (ADP)`
9950	`of (ADP) --[prep]--> aspect (NOUN)`
9951	`women (NOUN) --[poss]--> lives (NOUN)`
9952	`'s (PART) --[case]--> women (NOUN)`
9953	`lives (NOUN) --[pobj]--> of (ADP)`
9954	`, (PUNCT) --[punct]--> aspect (NOUN)`
9955	`from (ADP) --[prep]--> aspect (NOUN)`
9956	`marriage (NOUN) --[pobj]--> from (ADP)`
9957	`and (CCONJ) --[cc]--> marriage (NOUN)`
9958	`divorce (NOUN) --[conj]--> marriage (NOUN)`
9959	`and (CCONJ) --[cc]--> divorce (NOUN)`
9960	`inheritance (NOUN) --[conj]--> divorce (NOUN)`
9961	`to (PART) --[aux]--> travel (VERB)`
9962	`travel (VERB) --[advcl]--> aspect (NOUN)`
9963	`, (PUNCT) --[punct]--> travel (VERB)`
9964	`work (NOUN) --[conj]--> travel (VERB)`
9965	`and (CCONJ) --[cc]--> work (NOUN)`
9966	`legal (ADJ) --[amod]--> cases (NOUN)`
9967	`cases (NOUN) --[conj]--> work (NOUN)`
9968	`. (PUNCT) --[punct]--> grants (VERB)`
9969
9970	`Sentence 5: And notably, this is not the first time that Iranian women have courageously resisted these discriminatory practices.`
9971	`And (CCONJ) --[cc]--> is (AUX)`
9972	`notably (ADV) --[advmod]--> is (AUX)`
9973	`, (PUNCT) --[punct]--> is (AUX)`
9974	`this (PRON) --[nsubj]--> is (AUX)`
9975	`is (AUX) --[ROOT]--> is (AUX)`
9976	`not (PART) --[neg]--> is (AUX)`
9977	`the (DET) --[det]--> time (NOUN)`
9978	`first (ADJ) --[amod]--> time (NOUN)`
9979	`time (NOUN) --[attr]--> is (AUX)`
9980	`that (SCONJ) --[advmod]--> resisted (VERB)`
9981	`Iranian (ADJ) --[amod]--> women (NOUN)`
9982	`women (NOUN) --[nsubj]--> resisted (VERB)`
9983	`have (AUX) --[aux]--> resisted (VERB)`
9984	`courageously (ADV) --[advmod]--> resisted (VERB)`
9985	`resisted (VERB) --[relcl]--> time (NOUN)`
9986	`these (DET) --[det]--> practices (NOUN)`
9987	`discriminatory (ADJ) --[amod]--> practices (NOUN)`
9988	`practices (NOUN) --[dobj]--> resisted (VERB)`
9989	`. (PUNCT) --[punct]--> is (AUX)`
9990
9991	`Sentence 6: But it is the first time that these feminist demands, these demands for the basic equality of women, have sparked such a sustained, nationwide protest against dictatorship as a whole.`
9992	`But (CCONJ) --[cc]--> is (AUX)`
9993	`it (PRON) --[nsubj]--> is (AUX)`
9994	`is (AUX) --[ROOT]--> is (AUX)`
9995	`the (DET) --[det]--> time (NOUN)`
9996	`first (ADJ) --[amod]--> time (NOUN)`
9997	`time (NOUN) --[attr]--> is (AUX)`
9998	`that (PRON) --[advmod]--> sparked (VERB)`
9999	`these (DET) --[det]--> demands (NOUN)`
10000	`feminist (ADJ) --[amod]--> demands (NOUN)`
10001	`demands (NOUN) --[nsubj]--> sparked (VERB)`
10002	`, (PUNCT) --[punct]--> sparked (VERB)`
10003	`these (DET) --[det]--> demands (NOUN)`
10004	`demands (NOUN) --[nsubj]--> sparked (VERB)`
10005	`for (ADP) --[prep]--> demands (NOUN)`
10006	`the (DET) --[det]--> equality (NOUN)`
10007	`basic (ADJ) --[amod]--> equality (NOUN)`
10008	`equality (NOUN) --[pobj]--> for (ADP)`
10009	`of (ADP) --[prep]--> equality (NOUN)`
10010	`women (NOUN) --[pobj]--> of (ADP)`
10011	`, (PUNCT) --[punct]--> sparked (VERB)`
10012	`have (AUX) --[aux]--> sparked (VERB)`
10013	`sparked (VERB) --[relcl]--> time (NOUN)`
10014	`such (DET) --[predet]--> protest (NOUN)`
10015	`a (DET) --[det]--> protest (NOUN)`
10016	`sustained (ADJ) --[amod]--> protest (NOUN)`
10017	`, (PUNCT) --[punct]--> protest (NOUN)`
10018	`nationwide (ADJ) --[amod]--> protest (NOUN)`
10019	`protest (NOUN) --[dobj]--> sparked (VERB)`
10020	`against (ADP) --[prep]--> protest (NOUN)`
10021	`dictatorship (NOUN) --[pobj]--> against (ADP)`
10022	`as (ADP) --[prep]--> dictatorship (NOUN)`
10023	`a (DET) --[det]--> whole (NOUN)`
10024	`whole (NOUN) --[pobj]--> as (ADP)`
10025	`. (PUNCT) --[punct]--> is (AUX)`
10026
10027	`Sentence 7: Because all women in Iran face this severe oppression, their demands unify people across these religious and ethnic lines.`
10028	`Because (SCONJ) --[mark]--> face (VERB)`
10029	`all (DET) --[det]--> women (NOUN)`
10030	`women (NOUN) --[nsubj]--> face (VERB)`
10031	`in (ADP) --[prep]--> women (NOUN)`
10032	`Iran (PROPN) --[pobj]--> in (ADP)`
10033	`face (VERB) --[advcl]--> unify (VERB)`
10034	`this (DET) --[det]--> oppression (NOUN)`
10035	`severe (ADJ) --[amod]--> oppression (NOUN)`
10036	`oppression (NOUN) --[dobj]--> face (VERB)`
10037	`, (PUNCT) --[punct]--> unify (VERB)`
10038	`their (PRON) --[poss]--> demands (NOUN)`
10039	`demands (NOUN) --[nsubj]--> unify (VERB)`
10040	`unify (VERB) --[ROOT]--> unify (VERB)`
10041	`people (NOUN) --[dobj]--> unify (VERB)`
10042	`across (ADP) --[prep]--> people (NOUN)`
10043	`these (DET) --[det]--> lines (NOUN)`
10044	`religious (ADJ) --[amod]--> lines (NOUN)`
10045	`and (CCONJ) --[cc]--> religious (ADJ)`
10046	`ethnic (ADJ) --[conj]--> religious (ADJ)`
10047	`lines (NOUN) --[pobj]--> across (ADP)`
10048	`. (PUNCT) --[punct]--> unify (VERB)`
10049
10050	`Sentence 8: And because gender inequality is so foundational to the state and its repressive institutions, a demand for women's freedom is naturally, I would say, a demand for the democracy and freedom from all the kinds of human rights abuses that women and men alike suffer from there.`
10051	`And (CCONJ) --[cc]--> is (AUX)`
10052	`because (SCONJ) --[mark]--> is (AUX)`
10053	`gender (NOUN) --[compound]--> inequality (NOUN)`
10054	`inequality (NOUN) --[nsubj]--> is (AUX)`
10055	`is (AUX) --[advcl]--> is (AUX)`
10056	`so (ADV) --[advmod]--> foundational (ADJ)`
10057	`foundational (ADJ) --[acomp]--> is (AUX)`
10058	`to (ADP) --[prep]--> foundational (ADJ)`
10059	`the (DET) --[det]--> state (NOUN)`
10060	`state (NOUN) --[pobj]--> to (ADP)`
10061	`and (CCONJ) --[cc]--> foundational (ADJ)`
10062	`its (PRON) --[poss]--> institutions (NOUN)`
10063	`repressive (ADJ) --[amod]--> institutions (NOUN)`
10064	`institutions (NOUN) --[dep]--> is (AUX)`
10065	`, (PUNCT) --[punct]--> institutions (NOUN)`
10066	`a (DET) --[det]--> demand (NOUN)`
10067	`demand (NOUN) --[nsubj]--> is (AUX)`
10068	`for (ADP) --[prep]--> demand (NOUN)`
10069	`women (NOUN) --[poss]--> freedom (NOUN)`
10070	`'s (PART) --[case]--> women (NOUN)`
10071	`freedom (NOUN) --[pobj]--> for (ADP)`
10072	`is (AUX) --[ccomp]--> say (VERB)`
10073	`naturally (ADV) --[advmod]--> is (AUX)`
10074	`, (PUNCT) --[punct]--> say (VERB)`
10075	`I (PRON) --[nsubj]--> say (VERB)`
10076	`would (AUX) --[aux]--> say (VERB)`
10077	`say (VERB) --[ROOT]--> say (VERB)`
10078	`, (PUNCT) --[punct]--> say (VERB)`
10079	`a (DET) --[det]--> demand (NOUN)`
10080	`demand (NOUN) --[dobj]--> say (VERB)`
10081	`for (ADP) --[prep]--> demand (NOUN)`
10082	`the (DET) --[det]--> democracy (NOUN)`
10083	`democracy (NOUN) --[pobj]--> for (ADP)`
10084	`and (CCONJ) --[cc]--> democracy (NOUN)`
10085	`freedom (NOUN) --[conj]--> democracy (NOUN)`
10086	`from (ADP) --[prep]--> demand (NOUN)`
10087	`all (DET) --[predet]--> kinds (NOUN)`
10088	`the (DET) --[det]--> kinds (NOUN)`
10089	`kinds (NOUN) --[pobj]--> from (ADP)`
10090	`of (ADP) --[prep]--> kinds (NOUN)`
10091	`human (ADJ) --[amod]--> rights (NOUN)`
10092	`rights (NOUN) --[compound]--> abuses (NOUN)`
10093	`abuses (NOUN) --[pobj]--> of (ADP)`
10094	`that (SCONJ) --[pobj]--> from (ADP)`
10095	`women (NOUN) --[nsubj]--> suffer (VERB)`
10096	`and (CCONJ) --[cc]--> women (NOUN)`
10097	`men (NOUN) --[conj]--> women (NOUN)`
10098	`alike (ADV) --[advmod]--> women (NOUN)`
10099	`suffer (VERB) --[relcl]--> kinds (NOUN)`
10100	`from (ADP) --[prep]--> suffer (VERB)`
10101	`there (ADV) --[pcomp]--> from (ADP)`
10102	`. (PUNCT) --[punct]--> say (VERB)`
10103
10104	`Total sentences: 8`
10105
10106	`RAKE Keyphrase Relationships:`
10107	`noun phrase: "women" contains [women], [women], [women], [women], [women], [women], [women]`
10108	`noun phrase: "the current Iranian state" contains [current iranian state], [state]`
10109	`noun phrase: "these women" contains [women], [women], [women], [women], [women], [women], [women]`
10110	`noun phrase: "really the most intimate and personal aspect" contains [personal aspect], [really], [intimate]`
10111	`noun phrase: "women's lives" contains [women], [women], [women], [women], [women], [women], [women], [lives]`
10112	`noun phrase: "the first time" contains [first time], [first time], [first]`
10113	`noun phrase: "Iranian women" contains [iranian women], [women], [women], [women], [women], [women], [women], [women]`
10114	`noun phrase: "the first time" contains [first time], [first time], [first]`
10115	`noun phrase: "these feminist demands" contains [feminist demands], [demands], [demand], [demand]`
10116	`noun phrase: "these demands" contains [demands], [demand], [demand]`
10117	`noun phrase: "women" contains [women], [women], [women], [women], [women], [women], [women]`
10118	`noun phrase: "such a sustained, nationwide protest" contains [nationwide protest], [sustained]`
10119	`noun phrase: "all women" contains [women], [women], [women], [women], [women], [women], [women]`
10120	`noun phrase: "their demands" contains [demands], [demand], [demand]`
10121	`noun phrase: "these religious and ethnic lines" contains [ethnic lines], [religious]`
10122	`noun phrase: "a demand" contains [demand], [demand]`
10123	`noun phrase: "women's freedom" contains [women], [women], [women], [women], [women], [women], [women], [freedom], [freedom]`
10124	`noun phrase: "a demand" contains [demand], [demand]`
10125	`noun phrase: "freedom" contains [freedom], [freedom]`
10126	`noun phrase: "women" contains [women], [women], [women], [women], [women], [women], [women]`
10127	`verb phrase: "say would demand" contains [demand], [demand]`
10128	`Total relationships found: 21`
10129
10130	`YAKE Keyphrase Relationships:`
10131	`noun phrase: "the only oppressed group" contains [oppressed group], [oppressed], [group]`
10132	`noun phrase: "the current Iranian state" contains [current iranian state], [current iranian], [iranian], [iranian state], [current]`
10133	`noun phrase: "Iranian law" contains [iranian], [iranian law]`
10134	`noun phrase: "Iranian women" contains [women], [iranian women], [iranian]`
10135	`noun phrase: "these feminist demands" contains [demands], [demand]`
10136	`noun phrase: "these demands" contains [demands], [demand]`
10137	`noun phrase: "their demands" contains [demands], [demand]`
10138	`Total relationships found: 7`
10139
10140	`Timing Comparison:`
10141	`RAKE: 0.0017sec`
10142	`YAKE: 0.0252sec`
10143	`KeyBERT: 0.1424sec`
10144	`Dependencies: 0.0362sec`
10145	`Fastest: RAKE`
10146
10147	`================================================================================`
10148	`Message 74:`
10149	`KELLY: That's Emily Becker, a research scientist at the National Oceanic and Atmospheric Administration, NOAA. For those in her line of work, El Nino events also provide a chance to keep digging into a bigger looming question.`
10150	`--------------------------------------------------------------------------------`
10151	`RAKE Keywords:`
10152	`- bigger looming question (score: 9.00) → big looming question`
10153	`- research scientist (score: 4.00)`
10154	`- national oceanic (score: 4.00) → National Oceanic`
10155	`- keep digging (score: 4.00) → keep dig`
10156	`- emily becker (score: 4.00) → Emily Becker`
10157	`- atmospheric administration (score: 4.00) → Atmospheric Administration`
10158	`- work (score: 1.00)`
10159	`- noaa (score: 1.00) → NOAA`
10160	`- line (score: 1.00)`
10161	`- kelly (score: 1.00) → KELLY`
10162	`- chance (score: 1.00)`
10163	`Total keywords: 11 extracted in 0.0000 seconds`
10164
10165	`YAKE Keywords:`
10166	`- Emily Becker (score: 0.00) → Emily Becker`
10167	`- Atmospheric Administration (score: 0.00) → Atmospheric Administration`
10168	`- National Oceanic (score: 0.01) → National Oceanic`
10169	`- Oceanic and Atmospheric (score: 0.01) → Oceanic and Atmospheric`
10170	`- research scientist (score: 0.02)`
10171	`- KELLY (score: 0.04) → KELLY`
10172	`- NOAA (score: 0.04) → NOAA`
10173	`- bigger looming question (score: 0.05) → big looming question`
10174	`- Becker (score: 0.06) → Becker`
10175	`- Administration (score: 0.06) → Administration`
10176	`- Emily (score: 0.08) → Emily`
10177	`- National (score: 0.08) → National`
10178	`- Oceanic (score: 0.08) → Oceanic`
10179	`- Atmospheric (score: 0.08) → Atmospheric`
10180	`- Nino events (score: 0.09) → Nino event`
10181	`- line of work (score: 0.12)`
10182	`- looming question (score: 0.12)`
10183	`- research (score: 0.15)`
10184	`- scientist (score: 0.15)`
10185	`- events also provide (score: 0.17) → event also provide`
10186	`Total keywords: 20 extracted in 0.0160 seconds`
10187
10188	`KeyBERT Keywords:`
10189	`- el nino events (score: 0.63)`
10190	`- el nino (score: 0.62)`
10191	`- kelly emily becker (score: 0.58)`
10192	`- work el nino (score: 0.57)`
10193	`- scientist national oceanic (score: 0.55)`
10194	`- emily becker research (score: 0.54)`
10195	`- emily becker (score: 0.53)`
10196	`- nino events (score: 0.50)`
10197	`- national oceanic atmospheric (score: 0.49)`
10198	`- kelly emily (score: 0.49)`
10199	`- atmospheric administration noaa (score: 0.47)`
10200	`- nino events provide (score: 0.45)`
10201	`- oceanic atmospheric administration (score: 0.44)`
10202	`- becker research scientist (score: 0.44)`
10203	`- scientist national (score: 0.44)`
10204	`- oceanic atmospheric (score: 0.43)`
10205	`- noaa (score: 0.43)`
10206	`- nino (score: 0.43)`
10207	`- research scientist national (score: 0.42)`
10208	`- emily (score: 0.42)`
10209	`Total keywords: 20 extracted in 0.0352 seconds`
10210
10211	`Dependency Relations (extracted in 0.0097sec):`
10212
10213	`Sentence 1: KELLY:`
10214	`KELLY (PROPN) --[ROOT]--> KELLY (PROPN)`
10215	`: (PUNCT) --[punct]--> KELLY (PROPN)`
10216
10217	`Sentence 2: That's Emily Becker, a research scientist at the National Oceanic and Atmospheric Administration, NOAA.`
10218	`That (PRON) --[nsubj]--> 's (AUX)`
10219	`'s (AUX) --[ROOT]--> 's (AUX)`
10220	`Emily (PROPN) --[compound]--> Becker (PROPN)`
10221	`Becker (PROPN) --[attr]--> 's (AUX)`
10222	`, (PUNCT) --[punct]--> Becker (PROPN)`
10223	`a (DET) --[det]--> scientist (NOUN)`
10224	`research (NOUN) --[compound]--> scientist (NOUN)`
10225	`scientist (NOUN) --[appos]--> Becker (PROPN)`
10226	`at (ADP) --[prep]--> scientist (NOUN)`
10227	`the (DET) --[det]--> Oceanic (PROPN)`
10228	`National (PROPN) --[compound]--> Oceanic (PROPN)`
10229	`Oceanic (PROPN) --[pobj]--> at (ADP)`
10230	`and (CCONJ) --[cc]--> Oceanic (PROPN)`
10231	`Atmospheric (PROPN) --[compound]--> Administration (PROPN)`
10232	`Administration (PROPN) --[conj]--> Oceanic (PROPN)`
10233	`, (PUNCT) --[punct]--> Oceanic (PROPN)`
10234	`NOAA (PROPN) --[appos]--> Oceanic (PROPN)`
10235	`. (PUNCT) --[punct]--> 's (AUX)`
10236
10237	`Sentence 3: For those in her line of work, El Nino events also provide a chance to keep digging into a bigger looming question.`
10238	`For (ADP) --[prep]--> provide (VERB)`
10239	`those (PRON) --[pobj]--> For (ADP)`
10240	`in (ADP) --[prep]--> those (PRON)`
10241	`her (PRON) --[poss]--> line (NOUN)`
10242	`line (NOUN) --[pobj]--> in (ADP)`
10243	`of (ADP) --[prep]--> line (NOUN)`
10244	`work (NOUN) --[pobj]--> of (ADP)`
10245	`, (PUNCT) --[punct]--> provide (VERB)`
10246	`El (PROPN) --[compound]--> Nino (PROPN)`
10247	`Nino (PROPN) --[compound]--> events (NOUN)`
10248	`events (NOUN) --[nsubj]--> provide (VERB)`
10249	`also (ADV) --[advmod]--> provide (VERB)`
10250	`provide (VERB) --[ROOT]--> provide (VERB)`
10251	`a (DET) --[det]--> chance (NOUN)`
10252	`chance (NOUN) --[dobj]--> provide (VERB)`
10253	`to (PART) --[aux]--> keep (VERB)`
10254	`keep (VERB) --[acl]--> chance (NOUN)`
10255	`digging (VERB) --[xcomp]--> keep (VERB)`
10256	`into (ADP) --[prep]--> digging (VERB)`
10257	`a (DET) --[det]--> question (NOUN)`
10258	`bigger (ADJ) --[amod]--> question (NOUN)`
10259	`looming (NOUN) --[compound]--> question (NOUN)`
10260	`question (NOUN) --[pobj]--> into (ADP)`
10261	`. (PUNCT) --[punct]--> provide (VERB)`
10262
10263	`Total sentences: 3`
10264
10265	`RAKE Keyphrase Relationships:`
10266	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
10267
10268	`YAKE Keyphrase Relationships:`
10269	`noun phrase: "Emily Becker" contains [emily becker], [becker], [emily]`
10270	`noun phrase: "a research scientist" contains [research scientist], [research], [scientist]`
10271	`noun phrase: "the National Oceanic" contains [national oceanic], [national], [oceanic]`
10272	`noun phrase: "Atmospheric Administration" contains [atmospheric administration], [administration], [atmospheric]`
10273	`noun phrase: "a bigger looming question" contains [bigger looming question], [looming question]`
10274	`Total relationships found: 5`
10275
10276	`Timing Comparison:`
10277	`RAKE: 0.0000sec`
10278	`YAKE: 0.0160sec`
10279	`KeyBERT: 0.0352sec`
10280	`Dependencies: 0.0097sec`
10281	`Fastest: RAKE`
10282
10283	`================================================================================`
10284	`Message 75:`
10285	`SHAPIRO: That was four months ago, but she still feels traumatized by the journey.`
10286	`--------------------------------------------------------------------------------`
10287	`RAKE Keywords:`
10288	`- still feels traumatized (score: 9.00) → still feel traumatize`
10289	`- four months ago (score: 9.00) → four month ago`
10290	`- shapiro (score: 1.00) → SHAPIRO`
10291	`- journey (score: 1.00)`
10292	`Total keywords: 4 extracted in 0.0000 seconds`
10293
10294	`YAKE Keywords:`
10295	`- SHAPIRO (score: 0.03) → SHAPIRO`
10296	`- months ago (score: 0.05) → month ago`
10297	`- feels traumatized (score: 0.10) → feel traumatize`
10298	`- ago (score: 0.16)`
10299	`- journey (score: 0.16)`
10300	`- months (score: 0.30) → month`
10301	`- feels (score: 0.30) → feel`
10302	`- traumatized (score: 0.30) → traumatize`
10303	`Total keywords: 8 extracted in 0.0045 seconds`
10304
10305	`KeyBERT Keywords:`
10306	`- feels traumatized journey (score: 0.57)`
10307	`- shapiro months ago (score: 0.57)`
10308	`- ago feels traumatized (score: 0.55)`
10309	`- traumatized journey (score: 0.55)`
10310	`- shapiro months (score: 0.55)`
10311	`- feels traumatized (score: 0.50)`
10312	`- shapiro (score: 0.47)`
10313	`- traumatized (score: 0.44)`
10314	`- months ago feels (score: 0.42)`
10315	`- ago feels (score: 0.36)`
10316	`- journey (score: 0.34)`
10317	`- months (score: 0.28)`
10318	`- months ago (score: 0.28)`
10319	`- ago (score: 0.26)`
10320	`- feels (score: 0.23)`
10321	`Total keywords: 15 extracted in 0.0135 seconds`
10322
10323	`Dependency Relations (extracted in 0.0161sec):`
10324
10325	`Sentence 1: SHAPIRO: That was four months ago, but she still feels traumatized by the journey.`
10326	`SHAPIRO (PROPN) --[dep]--> was (AUX)`
10327	`: (PUNCT) --[punct]--> SHAPIRO (PROPN)`
10328	`That (PRON) --[nsubj]--> was (AUX)`
10329	`was (AUX) --[ROOT]--> was (AUX)`
10330	`four (NUM) --[nummod]--> months (NOUN)`
10331	`months (NOUN) --[npadvmod]--> ago (ADV)`
10332	`ago (ADV) --[advmod]--> was (AUX)`
10333	`, (PUNCT) --[punct]--> was (AUX)`
10334	`but (CCONJ) --[cc]--> was (AUX)`
10335	`she (PRON) --[nsubj]--> feels (VERB)`
10336	`still (ADV) --[advmod]--> feels (VERB)`
10337	`feels (VERB) --[conj]--> was (AUX)`
10338	`traumatized (VERB) --[acomp]--> feels (VERB)`
10339	`by (ADP) --[agent]--> traumatized (VERB)`
10340	`the (DET) --[det]--> journey (NOUN)`
10341	`journey (NOUN) --[pobj]--> by (ADP)`
10342	`. (PUNCT) --[punct]--> feels (VERB)`
10343
10344	`Total sentences: 1`
10345
10346	`RAKE Keyphrase Relationships:`
10347	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
10348
10349	`YAKE Keyphrase Relationships:`
10350	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
10351
10352	`Timing Comparison:`
10353	`RAKE: 0.0000sec`
10354	`YAKE: 0.0045sec`
10355	`KeyBERT: 0.0135sec`
10356	`Dependencies: 0.0161sec`
10357	`Fastest: RAKE`
10358
10359	`================================================================================`
10360	`Message 76:`
10361	BUSCH: I can't add any more detail really to what you already know. I do know from being in that building numerous times that if a gunman came in through the front door, it's very open. And, you know, if they're firing a gun, most of the people that would be in the newsroom would be very easily accessible being shot, and there's nowhere to run to really. So, I mean, it is a terrifying situation I'm sure if you were in there. And I have many friends that work at that newspaper, and I am very I guess anxious in some ways and - but, you know, I hope they're safe and - but five people have died. It's a very small news staff. It's a great newspaper, covers all local news and very friendly, very good, very community-oriented.
10362	`--------------------------------------------------------------------------------`
10363	`RAKE Keywords:`
10364	`- building numerous times (score: 9.00) → building numerous time`
10365	`- small news staff (score: 8.50)`
10366	`- local news (score: 4.50)`
10367	`- terrifying situation (score: 4.00)`
10368	`- many friends (score: 4.00) → many friend`
10369	`- gunman came (score: 4.00) → gunman come`
10370	`- guess anxious (score: 4.00)`
10371	`- front door (score: 4.00)`
10372	`- easily accessible (score: 4.00)`
10373	`- newsroom would (score: 3.50)`
10374	`- great newspaper (score: 3.50)`
10375	`- five people (score: 3.50)`
10376	`- detail really (score: 3.50)`
10377	`- already know (score: 3.25)`
10378	`- would (score: 1.50)`
10379	`- really (score: 1.50)`
10380	`- people (score: 1.50)`
10381	`- newspaper (score: 1.50)`
10382	`- know (score: 1.25)`
10383	`- know (score: 1.25)`
10384	`- know (score: 1.25)`
10385	`- work (score: 1.00)`
10386	`- ways (score: 1.00) → way`
10387	`- sure (score: 1.00)`
10388	`- shot (score: 1.00) → shoot`
10389	`- safe (score: 1.00)`
10390	`- run (score: 1.00)`
10391	`- oriented (score: 1.00) → orient`
10392	`- open (score: 1.00)`
10393	`- nowhere (score: 1.00)`
10394	`- mean (score: 1.00)`
10395	`- hope (score: 1.00)`
10396	`- gun (score: 1.00)`
10397	`- good (score: 1.00)`
10398	`- friendly (score: 1.00)`
10399	`- firing (score: 1.00) → fire`
10400	`- died (score: 1.00) → die`
10401	`- covers (score: 1.00) → cover`
10402	`- community (score: 1.00)`
10403	`- busch (score: 1.00) → BUSCH`
10404	`- add (score: 1.00)`
10405	`Total keywords: 41 extracted in 0.0000 seconds`
10406
10407	`YAKE Keywords:`
10408	`- BUSCH (score: 0.05) → BUSCH`
10409	`- building numerous times (score: 0.06) → building numerous time`
10410	`- front door (score: 0.12)`
10411	`- add (score: 0.14)`
10412	`- detail (score: 0.14)`
10413	`- building numerous (score: 0.15)`
10414	`- numerous times (score: 0.15) → numerous time`
10415	`- newspaper (score: 0.19)`
10416	`- people (score: 0.21)`
10417	`- firing a gun (score: 0.21) → fire a gun`
10418	`- accessible being shot (score: 0.21) → accessible being shoot`
10419	`- easily accessible (score: 0.25)`
10420	`- great newspaper (score: 0.28)`
10421	`- door (score: 0.31)`
10422	`- open (score: 0.31)`
10423	`- terrifying situation (score: 0.33)`
10424	`- building (score: 0.36)`
10425	`- numerous (score: 0.36)`
10426	`- times (score: 0.36) → time`
10427	`- gunman (score: 0.36)`
10428	`Total keywords: 20 extracted in 0.0177 seconds`
10429
10430	`KeyBERT Keywords:`
10431	`- gun people newsroom (score: 0.51)`
10432	`- busch (score: 0.45)`
10433	`- local news friendly (score: 0.45)`
10434	`- staff great newspaper (score: 0.45)`
10435	`- newspaper guess anxious (score: 0.44)`
10436	`- great newspaper (score: 0.44)`
10437	`- news staff (score: 0.44)`
10438	`- news staff great (score: 0.43)`
10439	`- work newspaper (score: 0.43)`
10440	`- newspaper (score: 0.43)`
10441	`- safe people died (score: 0.42)`
10442	`- small news staff (score: 0.42)`
10443	`- local news (score: 0.42)`
10444	`- friends work newspaper (score: 0.42)`
10445	`- news friendly (score: 0.42)`
10446	`- newspaper covers local (score: 0.41)`
10447	`- people newsroom (score: 0.40)`
10448	`- hope safe people (score: 0.40)`
10449	`- covers local news (score: 0.40)`
10450	`- news friendly good (score: 0.39)`
10451	`Total keywords: 20 extracted in 0.0791 seconds`
10452
10453	`Dependency Relations (extracted in 0.0258sec):`
10454
10455	`Sentence 1: BUSCH: I can't add any more detail really to what you already know.`
10456	`BUSCH (NOUN) --[ROOT]--> BUSCH (NOUN)`
10457	`: (PUNCT) --[punct]--> BUSCH (NOUN)`
10458	`I (PRON) --[nsubj]--> add (VERB)`
10459	`ca (AUX) --[aux]--> add (VERB)`
10460	`n't (PART) --[neg]--> add (VERB)`
10461	`add (VERB) --[ccomp]--> BUSCH (NOUN)`
10462	`any (DET) --[det]--> detail (NOUN)`
10463	`more (ADJ) --[amod]--> detail (NOUN)`
10464	`detail (NOUN) --[dobj]--> add (VERB)`
10465	`really (ADV) --[advmod]--> add (VERB)`
10466	`to (ADP) --[prep]--> add (VERB)`
10467	`what (PRON) --[dobj]--> know (VERB)`
10468	`you (PRON) --[nsubj]--> know (VERB)`
10469	`already (ADV) --[advmod]--> know (VERB)`
10470	`know (VERB) --[pcomp]--> to (ADP)`
10471	`. (PUNCT) --[punct]--> BUSCH (NOUN)`
10472
10473	`Sentence 2: I do know from being in that building numerous times that if a gunman came in through the front door, it's very open.`
10474	`I (PRON) --[nsubj]--> know (VERB)`
10475	`do (AUX) --[aux]--> know (VERB)`
10476	`know (VERB) --[ROOT]--> know (VERB)`
10477	`from (ADP) --[prep]--> know (VERB)`
10478	`being (AUX) --[pcomp]--> from (ADP)`
10479	`in (ADP) --[prep]--> being (AUX)`
10480	`that (DET) --[det]--> building (NOUN)`
10481	`building (NOUN) --[pobj]--> in (ADP)`
10482	`numerous (ADJ) --[amod]--> times (NOUN)`
10483	`times (NOUN) --[npadvmod]--> being (AUX)`
10484	`that (SCONJ) --[mark]--> 's (AUX)`
10485	`if (SCONJ) --[mark]--> came (VERB)`
10486	`a (DET) --[det]--> gunman (NOUN)`
10487	`gunman (NOUN) --[nsubj]--> came (VERB)`
10488	`came (VERB) --[advcl]--> 's (AUX)`
10489	`in (ADP) --[advmod]--> came (VERB)`
10490	`through (ADP) --[prep]--> came (VERB)`
10491	`the (DET) --[det]--> door (NOUN)`
10492	`front (ADJ) --[amod]--> door (NOUN)`
10493	`door (NOUN) --[pobj]--> through (ADP)`
10494	`, (PUNCT) --[punct]--> 's (AUX)`
10495	`it (PRON) --[nsubj]--> 's (AUX)`
10496	`'s (AUX) --[ccomp]--> know (VERB)`
10497	`very (ADV) --[advmod]--> open (ADJ)`
10498	`open (ADJ) --[acomp]--> 's (AUX)`
10499	`. (PUNCT) --[punct]--> know (VERB)`
10500
10501	`Sentence 3: And, you know, if they're firing a gun, most of the people that would be in the newsroom would be very easily accessible being shot, and there's nowhere to run to really.`
10502	`And (CCONJ) --[cc]--> be (AUX)`
10503	`, (PUNCT) --[punct]--> know (VERB)`
10504	`you (PRON) --[nsubj]--> know (VERB)`
10505	`know (VERB) --[parataxis]--> be (AUX)`
10506	`, (PUNCT) --[punct]--> be (AUX)`
10507	`if (SCONJ) --[mark]--> firing (VERB)`
10508	`they (PRON) --[nsubj]--> firing (VERB)`
10509	`'re (AUX) --[aux]--> firing (VERB)`
10510	`firing (VERB) --[advcl]--> be (AUX)`
10511	`a (DET) --[det]--> gun (NOUN)`
10512	`gun (NOUN) --[dobj]--> firing (VERB)`
10513	`, (PUNCT) --[punct]--> be (AUX)`
10514	`most (ADJ) --[nsubj]--> be (AUX)`
10515	`of (ADP) --[prep]--> most (ADJ)`
10516	`the (DET) --[det]--> people (NOUN)`
10517	`people (NOUN) --[pobj]--> of (ADP)`
10518	`that (PRON) --[nsubj]--> be (AUX)`
10519	`would (AUX) --[aux]--> be (AUX)`
10520	`be (AUX) --[relcl]--> people (NOUN)`
10521	`in (ADP) --[prep]--> be (AUX)`
10522	`the (DET) --[det]--> newsroom (NOUN)`
10523	`newsroom (NOUN) --[pobj]--> in (ADP)`
10524	`would (AUX) --[aux]--> be (AUX)`
10525	`be (AUX) --[ROOT]--> be (AUX)`
10526	`very (ADV) --[advmod]--> easily (ADV)`
10527	`easily (ADV) --[advmod]--> accessible (ADJ)`
10528	`accessible (ADJ) --[acomp]--> be (AUX)`
10529	`being (AUX) --[auxpass]--> shot (VERB)`
10530	`shot (VERB) --[xcomp]--> accessible (ADJ)`
10531	`, (PUNCT) --[punct]--> be (AUX)`
10532	`and (CCONJ) --[cc]--> be (AUX)`
10533	`there (PRON) --[expl]--> 's (VERB)`
10534	`'s (VERB) --[conj]--> be (AUX)`
10535	`nowhere (ADV) --[advmod]--> 's (VERB)`
10536	`to (PART) --[aux]--> run (VERB)`
10537	`run (VERB) --[xcomp]--> 's (VERB)`
10538	`to (ADP) --[prep]--> run (VERB)`
10539	`really (ADV) --[pcomp]--> to (ADP)`
10540	`. (PUNCT) --[punct]--> 's (VERB)`
10541
10542	`Sentence 4: So, I mean, it is a terrifying situation I'm sure if you were in there.`
10543	`So (ADV) --[advmod]--> is (AUX)`
10544	`, (PUNCT) --[punct]--> is (AUX)`
10545	`I (PRON) --[nsubj]--> mean (VERB)`
10546	`mean (VERB) --[parataxis]--> is (AUX)`
10547	`, (PUNCT) --[punct]--> is (AUX)`
10548	`it (PRON) --[nsubj]--> is (AUX)`
10549	`is (AUX) --[ROOT]--> is (AUX)`
10550	`a (DET) --[det]--> situation (NOUN)`
10551	`terrifying (ADJ) --[amod]--> situation (NOUN)`
10552	`situation (NOUN) --[attr]--> is (AUX)`
10553	`I (PRON) --[nsubj]--> 'm (AUX)`
10554	`'m (AUX) --[relcl]--> situation (NOUN)`
10555	`sure (ADJ) --[acomp]--> 'm (AUX)`
10556	`if (SCONJ) --[mark]--> were (AUX)`
10557	`you (PRON) --[nsubj]--> were (AUX)`
10558	`were (AUX) --[advcl]--> 'm (AUX)`
10559	`in (ADV) --[advmod]--> there (ADV)`
10560	`there (ADV) --[advmod]--> were (AUX)`
10561	`. (PUNCT) --[punct]--> is (AUX)`
10562
10563	`Sentence 5: And I have many friends that work at that newspaper, and I am very I guess anxious in some ways and -`
10564	`And (CCONJ) --[cc]--> have (VERB)`
10565	`I (PRON) --[nsubj]--> have (VERB)`
10566	`have (VERB) --[ROOT]--> have (VERB)`
10567	`many (ADJ) --[amod]--> friends (NOUN)`
10568	`friends (NOUN) --[dobj]--> have (VERB)`
10569	`that (PRON) --[nsubj]--> work (VERB)`
10570	`work (VERB) --[relcl]--> friends (NOUN)`
10571	`at (ADP) --[prep]--> work (VERB)`
10572	`that (DET) --[det]--> newspaper (NOUN)`
10573	`newspaper (NOUN) --[pobj]--> at (ADP)`
10574	`, (PUNCT) --[punct]--> have (VERB)`
10575	`and (CCONJ) --[cc]--> have (VERB)`
10576	`I (PRON) --[nsubj]--> am (AUX)`
10577	`am (AUX) --[conj]--> have (VERB)`
10578	`very (ADV) --[acomp]--> am (AUX)`
10579	`I (PRON) --[nsubj]--> guess (VERB)`
10580	`guess (VERB) --[parataxis]--> am (AUX)`
10581	`anxious (ADJ) --[acomp]--> guess (VERB)`
10582	`in (ADP) --[prep]--> guess (VERB)`
10583	`some (DET) --[det]--> ways (NOUN)`
10584	`ways (NOUN) --[pobj]--> in (ADP)`
10585	`and (CCONJ) --[cc]--> am (AUX)`
10586	`- (PUNCT) --[punct]--> am (AUX)`
10587
10588	`Sentence 6: but, you know, I hope they're safe and - but five people have died.`
10589	`but (CCONJ) --[cc]--> hope (VERB)`
10590	`, (PUNCT) --[punct]--> hope (VERB)`
10591	`you (PRON) --[nsubj]--> know (VERB)`
10592	`know (VERB) --[parataxis]--> hope (VERB)`
10593	`, (PUNCT) --[punct]--> hope (VERB)`
10594	`I (PRON) --[nsubj]--> hope (VERB)`
10595	`hope (VERB) --[ROOT]--> hope (VERB)`
10596	`they (PRON) --[nsubj]--> 're (AUX)`
10597	`'re (AUX) --[ccomp]--> hope (VERB)`
10598	`safe (ADJ) --[acomp]--> 're (AUX)`
10599	`and (CCONJ) --[cc]--> 're (AUX)`
10600	`- (PUNCT) --[punct]--> hope (VERB)`
10601	`but (CCONJ) --[cc]--> hope (VERB)`
10602	`five (NUM) --[nummod]--> people (NOUN)`
10603	`people (NOUN) --[nsubj]--> died (VERB)`
10604	`have (AUX) --[aux]--> died (VERB)`
10605	`died (VERB) --[conj]--> hope (VERB)`
10606	`. (PUNCT) --[punct]--> died (VERB)`
10607
10608	`Sentence 7: It's a very small news staff.`
10609	`It (PRON) --[nsubj]--> 's (AUX)`
10610	`'s (AUX) --[ROOT]--> 's (AUX)`
10611	`a (DET) --[det]--> staff (NOUN)`
10612	`very (ADV) --[advmod]--> small (ADJ)`
10613	`small (ADJ) --[amod]--> staff (NOUN)`
10614	`news (NOUN) --[compound]--> staff (NOUN)`
10615	`staff (NOUN) --[attr]--> 's (AUX)`
10616	`. (PUNCT) --[punct]--> 's (AUX)`
10617
10618	`Sentence 8: It's a great newspaper, covers all local news and very friendly, very good, very community-oriented.`
10619	`It (PRON) --[nsubj]--> 's (AUX)`
10620	`'s (AUX) --[ccomp]--> covers (VERB)`
10621	`a (DET) --[det]--> newspaper (NOUN)`
10622	`great (ADJ) --[amod]--> newspaper (NOUN)`
10623	`newspaper (NOUN) --[attr]--> 's (AUX)`
10624	`, (PUNCT) --[punct]--> covers (VERB)`
10625	`covers (VERB) --[ROOT]--> covers (VERB)`
10626	`all (DET) --[det]--> news (NOUN)`
10627	`local (ADJ) --[amod]--> news (NOUN)`
10628	`news (NOUN) --[dobj]--> covers (VERB)`
10629	`and (CCONJ) --[cc]--> news (NOUN)`
10630	`very (ADV) --[advmod]--> friendly (ADJ)`
10631	`friendly (ADJ) --[amod]--> oriented (VERB)`
10632	`, (PUNCT) --[punct]--> friendly (ADJ)`
10633	`very (ADV) --[advmod]--> good (ADJ)`
10634	`good (ADJ) --[conj]--> friendly (ADJ)`
10635	`, (PUNCT) --[punct]--> good (ADJ)`
10636	`very (ADV) --[advmod]--> oriented (VERB)`
10637	`community (NOUN) --[npadvmod]--> oriented (VERB)`
10638	`- (PUNCT) --[punct]--> oriented (VERB)`
10639	`oriented (VERB) --[ccomp]--> covers (VERB)`
10640	`. (PUNCT) --[punct]--> covers (VERB)`
10641
10642	`Total sentences: 8`
10643
10644	`RAKE Keyphrase Relationships:`
10645	`noun phrase: "five people" contains [five people], [people]`
10646	`noun phrase: "a great newspaper" contains [great newspaper], [newspaper]`
10647	`verb phrase: "add ca n't detail really to" contains [detail really], [really], [add]`
10648	`verb phrase: "know what already" contains [know], [know], [know]`
10649	`verb phrase: "know do from" contains [know], [know], [know]`
10650	`verb phrase: "firing 're gun" contains [gun], [firing]`
10651	`Total relationships found: 6`
10652
10653	`YAKE Keyphrase Relationships:`
10654	`noun phrase: "the front door" contains [front door], [door]`
10655	`noun phrase: "a great newspaper" contains [newspaper], [great newspaper]`
10656	`verb phrase: "add ca n't detail really to" contains [add], [detail]`
10657	`Total relationships found: 3`
10658
10659	`Timing Comparison:`
10660	`RAKE: 0.0000sec`
10661	`YAKE: 0.0177sec`
10662	`KeyBERT: 0.0791sec`
10663	`Dependencies: 0.0258sec`
10664	`Fastest: RAKE`
10665
10666	`================================================================================`
10667	`Message 77:`
10668	`MAIER: It's because we both have a commitment to listening. And when we sit down at the table with each other, we come with an open mind, really wanting to know what the other person thinks about an issue and not trying to win the argument. We know that we're not going to change each other's opinions on anything, and that's not our goal. Our goal is really curiosity and an open heart.`
10669	`--------------------------------------------------------------------------------`
10670	`RAKE Keywords:`
10671	`- really wanting (score: 4.00) → really want`
10672	`- really curiosity (score: 4.00)`
10673	`- person thinks (score: 4.00) → person think`
10674	`- open mind (score: 4.00)`
10675	`- open heart (score: 4.00)`
10676	`- win (score: 1.00)`
10677	`- trying (score: 1.00) → try`
10678	`- table (score: 1.00)`
10679	`- sit (score: 1.00)`
10680	`- opinions (score: 1.00) → opinion`
10681	`- maier (score: 1.00) → MAIER`
10682	`- listening (score: 1.00) → listen`
10683	`- know (score: 1.00)`
10684	`- know (score: 1.00)`
10685	`- issue (score: 1.00)`
10686	`- going (score: 1.00) → go`
10687	`- goal (score: 1.00)`
10688	`- goal (score: 1.00)`
10689	`- commitment (score: 1.00)`
10690	`- come (score: 1.00)`
10691	`- change (score: 1.00)`
10692	`- argument (score: 1.00)`
10693	`- anything (score: 1.00)`
10694	`Total keywords: 23 extracted in 0.0000 seconds`
10695
10696	`YAKE Keywords:`
10697	`- commitment to listening (score: 0.02) → commitment to listen`
10698	`- MAIER (score: 0.05) → MAIER`
10699	`- listening (score: 0.13) → listen`
10700	`- open mind (score: 0.15)`
10701	`- win the argument (score: 0.16)`
10702	`- commitment (score: 0.16)`
10703	`- open (score: 0.20)`
10704	`- goal (score: 0.21)`
10705	`- open heart (score: 0.23)`
10706	`- mind (score: 0.33)`
10707	`- argument (score: 0.33)`
10708	`- sit (score: 0.40)`
10709	`- table (score: 0.40)`
10710	`- wanting (score: 0.40) → want`
10711	`- person (score: 0.40)`
10712	`- issue (score: 0.40)`
10713	`- win (score: 0.40)`
10714	`- heart (score: 0.47)`
10715	`- change (score: 0.50)`
10716	`- opinions (score: 0.50) → opinion`
10717	`Total keywords: 20 extracted in 0.0115 seconds`
10718
10719	`KeyBERT Keywords:`
10720	`- maier commitment listening (score: 0.43)`
10721	`- commitment listening (score: 0.41)`
10722	`- commitment listening sit (score: 0.39)`
10723	`- person thinks issue (score: 0.31)`
10724	`- listening (score: 0.30)`
10725	`- goal really curiosity (score: 0.29)`
10726	`- listening sit (score: 0.29)`
10727	`- curiosity open heart (score: 0.28)`
10728	`- curiosity (score: 0.28)`
10729	`- open mind (score: 0.28)`
10730	`- listening sit table (score: 0.27)`
10731	`- change opinions goal (score: 0.27)`
10732	`- open mind really (score: 0.26)`
10733	`- mind really wanting (score: 0.26)`
10734	`- maier commitment (score: 0.24)`
10735	`- opinions goal goal (score: 0.23)`
10736	`- opinions goal (score: 0.23)`
10737	`- change opinions (score: 0.22)`
10738	`- mind (score: 0.22)`
10739	`- person thinks (score: 0.22)`
10740	`Total keywords: 20 extracted in 0.0498 seconds`
10741
10742	`Dependency Relations (extracted in 0.0139sec):`
10743
10744	`Sentence 1: MAIER:`
10745	`MAIER (PROPN) --[ROOT]--> MAIER (PROPN)`
10746	`: (PUNCT) --[punct]--> MAIER (PROPN)`
10747
10748	`Sentence 2: It's because we both have a commitment to listening.`
10749	`It (PRON) --[nsubj]--> 's (AUX)`
10750	`'s (AUX) --[ROOT]--> 's (AUX)`
10751	`because (SCONJ) --[mark]--> have (VERB)`
10752	`we (PRON) --[nsubj]--> have (VERB)`
10753	`both (PRON) --[appos]--> we (PRON)`
10754	`have (VERB) --[advcl]--> 's (AUX)`
10755	`a (DET) --[det]--> commitment (NOUN)`
10756	`commitment (NOUN) --[dobj]--> have (VERB)`
10757	`to (ADP) --[prep]--> commitment (NOUN)`
10758	`listening (VERB) --[pcomp]--> to (ADP)`
10759	`. (PUNCT) --[punct]--> 's (AUX)`
10760
10761	`Sentence 3: And when we sit down at the table with each other, we come with an open mind, really wanting to know what the other person thinks about an issue and not trying to win the argument.`
10762	`And (CCONJ) --[cc]--> come (VERB)`
10763	`when (SCONJ) --[advmod]--> sit (VERB)`
10764	`we (PRON) --[nsubj]--> sit (VERB)`
10765	`sit (VERB) --[advcl]--> come (VERB)`
10766	`down (ADP) --[prt]--> sit (VERB)`
10767	`at (ADP) --[prep]--> sit (VERB)`
10768	`the (DET) --[det]--> table (NOUN)`
10769	`table (NOUN) --[pobj]--> at (ADP)`
10770	`with (ADP) --[prep]--> sit (VERB)`
10771	`each (DET) --[det]--> other (ADJ)`
10772	`other (ADJ) --[pobj]--> with (ADP)`
10773	`, (PUNCT) --[punct]--> come (VERB)`
10774	`we (PRON) --[nsubj]--> come (VERB)`
10775	`come (VERB) --[ROOT]--> come (VERB)`
10776	`with (ADP) --[prep]--> come (VERB)`
10777	`an (DET) --[det]--> mind (NOUN)`
10778	`open (ADJ) --[amod]--> mind (NOUN)`
10779	`mind (NOUN) --[pobj]--> with (ADP)`
10780	`, (PUNCT) --[punct]--> come (VERB)`
10781	`really (ADV) --[advmod]--> wanting (VERB)`
10782	`wanting (VERB) --[advcl]--> come (VERB)`
10783	`to (PART) --[aux]--> know (VERB)`
10784	`know (VERB) --[xcomp]--> wanting (VERB)`
10785	`what (PRON) --[dobj]--> thinks (VERB)`
10786	`the (DET) --[det]--> person (NOUN)`
10787	`other (ADJ) --[amod]--> person (NOUN)`
10788	`person (NOUN) --[nsubj]--> thinks (VERB)`
10789	`thinks (VERB) --[ccomp]--> know (VERB)`
10790	`about (ADP) --[prep]--> thinks (VERB)`
10791	`an (DET) --[det]--> issue (NOUN)`
10792	`issue (NOUN) --[pobj]--> about (ADP)`
10793	`and (CCONJ) --[cc]--> thinks (VERB)`
10794	`not (PART) --[neg]--> trying (VERB)`
10795	`trying (VERB) --[conj]--> thinks (VERB)`
10796	`to (PART) --[aux]--> win (VERB)`
10797	`win (VERB) --[xcomp]--> trying (VERB)`
10798	`the (DET) --[det]--> argument (NOUN)`
10799	`argument (NOUN) --[dobj]--> win (VERB)`
10800	`. (PUNCT) --[punct]--> come (VERB)`
10801
10802	`Sentence 4: We know that we're not going to change each other's opinions on anything, and that's not our goal.`
10803	`We (PRON) --[nsubj]--> know (VERB)`
10804	`know (VERB) --[ROOT]--> know (VERB)`
10805	`that (SCONJ) --[mark]--> going (VERB)`
10806	`we (PRON) --[nsubj]--> going (VERB)`
10807	`'re (AUX) --[aux]--> going (VERB)`
10808	`not (PART) --[neg]--> going (VERB)`
10809	`going (VERB) --[ccomp]--> know (VERB)`
10810	`to (PART) --[aux]--> change (VERB)`
10811	`change (VERB) --[xcomp]--> going (VERB)`
10812	`each (DET) --[det]--> other (ADJ)`
10813	`other (ADJ) --[poss]--> opinions (NOUN)`
10814	`'s (PART) --[case]--> other (ADJ)`
10815	`opinions (NOUN) --[dobj]--> change (VERB)`
10816	`on (ADP) --[prep]--> opinions (NOUN)`
10817	`anything (PRON) --[pobj]--> on (ADP)`
10818	`, (PUNCT) --[punct]--> know (VERB)`
10819	`and (CCONJ) --[cc]--> know (VERB)`
10820	`that (PRON) --[nsubj]--> 's (AUX)`
10821	`'s (AUX) --[conj]--> know (VERB)`
10822	`not (PART) --[neg]--> 's (AUX)`
10823	`our (PRON) --[poss]--> goal (NOUN)`
10824	`goal (NOUN) --[attr]--> 's (AUX)`
10825	`. (PUNCT) --[punct]--> 's (AUX)`
10826
10827	`Sentence 5: Our goal is really curiosity and an open heart.`
10828	`Our (PRON) --[poss]--> goal (NOUN)`
10829	`goal (NOUN) --[nsubj]--> is (AUX)`
10830	`is (AUX) --[ROOT]--> is (AUX)`
10831	`really (ADV) --[advmod]--> is (AUX)`
10832	`curiosity (NOUN) --[attr]--> is (AUX)`
10833	`and (CCONJ) --[cc]--> curiosity (NOUN)`
10834	`an (DET) --[det]--> heart (NOUN)`
10835	`open (ADJ) --[amod]--> heart (NOUN)`
10836	`heart (NOUN) --[conj]--> curiosity (NOUN)`
10837	`. (PUNCT) --[punct]--> is (AUX)`
10838
10839	`Total sentences: 5`
10840
10841	`RAKE Keyphrase Relationships:`
10842	`noun phrase: "our goal" contains [goal], [goal]`
10843	`noun phrase: "Our goal" contains [goal], [goal]`
10844	`verb phrase: "know to" contains [know], [know]`
10845	`verb phrase: "win to argument" contains [win], [argument]`
10846	`verb phrase: "change to opinions" contains [opinions], [change]`
10847	`Total relationships found: 5`
10848
10849	`YAKE Keyphrase Relationships:`
10850	`noun phrase: "an open mind" contains [open mind], [open], [mind]`
10851	`noun phrase: "an open heart" contains [open], [open heart], [heart]`
10852	`verb phrase: "win to argument" contains [argument], [win]`
10853	`verb phrase: "change to opinions" contains [change], [opinions]`
10854	`Total relationships found: 4`
10855
10856	`Timing Comparison:`
10857	`RAKE: 0.0000sec`
10858	`YAKE: 0.0115sec`
10859	`KeyBERT: 0.0498sec`
10860	`Dependencies: 0.0139sec`
10861	`Fastest: RAKE`
10862
10863	`================================================================================`
10864	`Message 78:`
10865	`SEEMA LAKDAWALA: When we recommend people get a flu vaccine shot, it is not a hundred percent effective. At best, it's 80%, but normally, it's around 60.`
10866	`--------------------------------------------------------------------------------`
10867	`RAKE Keywords:`
10868	`- recommend people get (score: 9.00)`
10869	`- hundred percent effective (score: 9.00)`
10870	`- flu vaccine shot (score: 9.00)`
10871	`- seema lakdawala (score: 4.00) → SEEMA LAKDAWALA`
10872	`- around 60 (score: 4.00)`
10873	`- 80 %, (score: 4.00)`
10874	`- normally (score: 1.00)`
10875	`- best (score: 1.00) → well`
10876	`Total keywords: 8 extracted in 0.0000 seconds`
10877
10878	`YAKE Keywords:`
10879	`- flu vaccine shot (score: 0.00)`
10880	`- hundred percent effective (score: 0.00)`
10881	`- SEEMA LAKDAWALA (score: 0.00) → SEEMA LAKDAWALA`
10882	`- vaccine shot (score: 0.02)`
10883	`- percent effective (score: 0.02)`
10884	`- recommend people (score: 0.02)`
10885	`- flu vaccine (score: 0.02)`
10886	`- hundred percent (score: 0.02)`
10887	`- SEEMA (score: 0.06) → SEEMA`
10888	`- LAKDAWALA (score: 0.06) → LAKDAWALA`
10889	`- shot (score: 0.10)`
10890	`- effective (score: 0.10)`
10891	`- recommend (score: 0.15)`
10892	`- people (score: 0.15)`
10893	`- flu (score: 0.15)`
10894	`- vaccine (score: 0.15)`
10895	`- hundred (score: 0.15)`
10896	`- percent (score: 0.15)`
10897	`Total keywords: 18 extracted in 0.0132 seconds`
10898
10899	`KeyBERT Keywords:`
10900	`- vaccine shot percent (score: 0.73)`
10901	`- flu vaccine shot (score: 0.69)`
10902	`- shot percent effective (score: 0.64)`
10903	`- flu vaccine (score: 0.63)`
10904	`- people flu vaccine (score: 0.62)`
10905	`- shot percent (score: 0.57)`
10906	`- vaccine shot (score: 0.55)`
10907	`- vaccine (score: 0.47)`
10908	`- percent effective best (score: 0.46)`
10909	`- percent effective (score: 0.46)`
10910	`- recommend people flu (score: 0.46)`
10911	`- people flu (score: 0.41)`
10912	`- flu (score: 0.38)`
10913	`- percent (score: 0.32)`
10914	`- shot (score: 0.28)`
10915	`- effective best 80 (score: 0.27)`
10916	`- effective best (score: 0.26)`
10917	`- effective (score: 0.26)`
10918	`- normally 60 (score: 0.22)`
10919	`- 80 normally 60 (score: 0.20)`
10920	`Total keywords: 20 extracted in 0.0378 seconds`
10921
10922	`Dependency Relations (extracted in 0.0100sec):`
10923
10924	`Sentence 1: SEEMA LAKDAWALA: When we recommend people get a flu vaccine shot, it is not a hundred percent effective.`
10925	`SEEMA (PROPN) --[compound]--> LAKDAWALA (NOUN)`
10926	`LAKDAWALA (NOUN) --[ROOT]--> LAKDAWALA (NOUN)`
10927	`: (PUNCT) --[punct]--> LAKDAWALA (NOUN)`
10928	`When (SCONJ) --[advmod]--> recommend (VERB)`
10929	`we (PRON) --[nsubj]--> recommend (VERB)`
10930	`recommend (VERB) --[advcl]--> is (AUX)`
10931	`people (NOUN) --[nsubj]--> get (VERB)`
10932	`get (VERB) --[ccomp]--> recommend (VERB)`
10933	`a (DET) --[det]--> vaccine (NOUN)`
10934	`flu (NOUN) --[compound]--> vaccine (NOUN)`
10935	`vaccine (NOUN) --[nsubj]--> shot (NOUN)`
10936	`shot (NOUN) --[dobj]--> get (VERB)`
10937	`, (PUNCT) --[punct]--> is (AUX)`
10938	`it (PRON) --[nsubj]--> is (AUX)`
10939	`is (AUX) --[acl]--> LAKDAWALA (NOUN)`
10940	`not (PART) --[neg]--> is (AUX)`
10941	`a (DET) --[quantmod]--> hundred (NUM)`
10942	`hundred (NUM) --[nummod]--> percent (NOUN)`
10943	`percent (NOUN) --[npadvmod]--> effective (ADJ)`
10944	`effective (ADJ) --[acomp]--> is (AUX)`
10945	`. (PUNCT) --[punct]--> LAKDAWALA (NOUN)`
10946
10947	`Sentence 2: At best, it's 80%, but normally, it's around 60.`
10948	`At (ADP) --[advmod]--> best (ADV)`
10949	`best (ADV) --[advmod]--> 's (AUX)`
10950	`, (PUNCT) --[punct]--> 's (AUX)`
10951	`it (PRON) --[nsubj]--> 's (AUX)`
10952	`'s (AUX) --[ROOT]--> 's (AUX)`
10953	`80 (NUM) --[nummod]--> % (NOUN)`
10954	`% (NOUN) --[attr]--> 's (AUX)`
10955	`, (PUNCT) --[punct]--> 's (AUX)`
10956	`but (CCONJ) --[cc]--> 's (AUX)`
10957	`normally (ADV) --[advmod]--> 's (AUX)`
10958	`, (PUNCT) --[punct]--> 's (AUX)`
10959	`it (PRON) --[nsubj]--> 's (AUX)`
10960	`'s (AUX) --[conj]--> 's (AUX)`
10961	`around (ADV) --[advmod]--> 60 (NUM)`
10962	`60 (NUM) --[attr]--> 's (AUX)`
10963	`. (PUNCT) --[punct]--> 's (AUX)`
10964
10965	`Total sentences: 2`
10966
10967	`RAKE Keyphrase Relationships:`
10968	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
10969
10970	`YAKE Keyphrase Relationships:`
10971	`noun phrase: "SEEMA LAKDAWALA" contains [seema lakdawala], [seema], [lakdawala]`
10972	`noun phrase: "a flu vaccine" contains [flu vaccine], [flu], [vaccine]`
10973	`Total relationships found: 2`
10974
10975	`Timing Comparison:`
10976	`RAKE: 0.0000sec`
10977	`YAKE: 0.0132sec`
10978	`KeyBERT: 0.0378sec`
10979	`Dependencies: 0.0100sec`
10980	`Fastest: RAKE`
10981
10982	`================================================================================`
10983	`Message 79:`
10984	`RICHARDSON: (Singing) I was standing by my window.Play it.`
10985	`--------------------------------------------------------------------------------`
10986	`RAKE Keywords:`
10987	`- window (score: 1.00)`
10988	`- standing (score: 1.00) → stand`
10989	`- singing (score: 1.00)`
10990	`- richardson (score: 1.00) → RICHARDSON`
10991	`- play (score: 1.00)`
10992	`Total keywords: 5 extracted in 0.0000 seconds`
10993
10994	`YAKE Keywords:`
10995	`- RICHARDSON (score: 0.03) → RICHARDSON`
10996	`- Singing (score: 0.03)`
10997	`- standing (score: 0.30) → stand`
10998	`- window.Play (score: 0.30)`
10999	`Total keywords: 4 extracted in 0.0017 seconds`
11000
11001	`KeyBERT Keywords:`
11002	`- richardson singing standing (score: 0.76)`
11003	`- richardson singing (score: 0.73)`
11004	`- singing standing window (score: 0.67)`
11005	`- standing window play (score: 0.57)`
11006	`- richardson (score: 0.55)`
11007	`- singing standing (score: 0.50)`
11008	`- window play (score: 0.50)`
11009	`- standing window (score: 0.48)`
11010	`- singing (score: 0.43)`
11011	`- window (score: 0.40)`
11012	`- play (score: 0.38)`
11013	`- standing (score: 0.32)`
11014	`Total keywords: 12 extracted in 0.0201 seconds`
11015
11016	`Dependency Relations (extracted in 0.0040sec):`
11017
11018	`Sentence 1: RICHARDSON: (Singing) I was standing by my window.`
11019	`RICHARDSON (PROPN) --[npadvmod]--> standing (VERB)`
11020	`: (PUNCT) --[punct]--> RICHARDSON (PROPN)`
11021	`( (PUNCT) --[punct]--> Singing (NOUN)`
11022	`Singing (NOUN) --[appos]--> RICHARDSON (PROPN)`
11023	`) (PUNCT) --[punct]--> Singing (NOUN)`
11024	`I (PRON) --[nsubj]--> standing (VERB)`
11025	`was (AUX) --[aux]--> standing (VERB)`
11026	`standing (VERB) --[ROOT]--> standing (VERB)`
11027	`by (ADP) --[prep]--> standing (VERB)`
11028	`my (PRON) --[poss]--> window (NOUN)`
11029	`window (NOUN) --[pobj]--> by (ADP)`
11030	`. (PUNCT) --[punct]--> standing (VERB)`
11031
11032	`Sentence 2: Play it.`
11033	`Play (VERB) --[ROOT]--> Play (VERB)`
11034	`it (PRON) --[dobj]--> Play (VERB)`
11035	`. (PUNCT) --[punct]--> Play (VERB)`
11036
11037	`Total sentences: 2`
11038
11039	`RAKE Keyphrase Relationships:`
11040	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
11041
11042	`YAKE Keyphrase Relationships:`
11043	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
11044
11045	`Timing Comparison:`
11046	`RAKE: 0.0000sec`
11047	`YAKE: 0.0017sec`
11048	`KeyBERT: 0.0201sec`
11049	`Dependencies: 0.0040sec`
11050	`Fastest: RAKE`
11051
11052	`================================================================================`
11053	`Message 80:`
11054	`TIM MAK: Hey there.`
11055	`--------------------------------------------------------------------------------`
11056	`RAKE Keywords:`
11057	`- tim mak (score: 4.00) → TIM MAK`
11058	`- hey (score: 1.00)`
11059	`Total keywords: 2 extracted in 0.0000 seconds`
11060
11061	`YAKE Keywords:`
11062	`- TIM MAK (score: 0.01) → TIM MAK`
11063	`- TIM (score: 0.09) → TIM`
11064	`- MAK (score: 0.09) → MAK`
11065	`- Hey (score: 0.09)`
11066	`Total keywords: 4 extracted in 0.0000 seconds`
11067
11068	`KeyBERT Keywords:`
11069	`- tim mak hey (score: 0.86)`
11070	`- tim mak (score: 0.77)`
11071	`- mak hey (score: 0.63)`
11072	`- tim (score: 0.62)`
11073	`- mak (score: 0.51)`
11074	`- hey (score: 0.41)`
11075	`Total keywords: 6 extracted in 0.0113 seconds`
11076
11077	`Dependency Relations (extracted in 0.0057sec):`
11078
11079	`Sentence 1: TIM MAK: Hey there.`
11080	`TIM (PROPN) --[compound]--> MAK (PROPN)`
11081	`MAK (PROPN) --[ROOT]--> MAK (PROPN)`
11082	`: (PUNCT) --[punct]--> MAK (PROPN)`
11083	`Hey (INTJ) --[intj]--> MAK (PROPN)`
11084	`there (ADV) --[advmod]--> Hey (INTJ)`
11085	`. (PUNCT) --[punct]--> MAK (PROPN)`
11086
11087	`Total sentences: 1`
11088
11089	`RAKE Keyphrase Relationships:`
11090	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
11091
11092	`YAKE Keyphrase Relationships:`
11093	`noun phrase: "TIM MAK" contains [tim mak], [tim], [mak]`
11094	`Total relationships found: 1`
11095
11096	`Timing Comparison:`
11097	`RAKE: 0.0000sec`
11098	`YAKE: 0.0000sec`
11099	`KeyBERT: 0.0113sec`
11100	`Dependencies: 0.0057sec`
11101	`Fastest: RAKE`
11102
11103	`================================================================================`
11104	`Message 81:`
11105	`BOUDIN: Thank you. Have a great day.`
11106	`--------------------------------------------------------------------------------`
11107	`RAKE Keywords:`
11108	`- great day (score: 4.00)`
11109	`- thank (score: 1.00)`
11110	`- boudin (score: 1.00) → BOUDIN`
11111	`Total keywords: 3 extracted in 0.0000 seconds`
11112
11113	`YAKE Keywords:`
11114	`- BOUDIN (score: 0.04) → BOUDIN`
11115	`- great day (score: 0.45)`
11116	`- day (score: 0.47)`
11117	`- great (score: 0.66)`
11118	`Total keywords: 4 extracted in 0.0000 seconds`
11119
11120	`KeyBERT Keywords:`
11121	`- boudin thank great (score: 0.78)`
11122	`- boudin thank (score: 0.75)`
11123	`- boudin (score: 0.64)`
11124	`- thank great day (score: 0.40)`
11125	`- great day (score: 0.34)`
11126	`- thank great (score: 0.28)`
11127	`- day (score: 0.25)`
11128	`- thank (score: 0.23)`
11129	`- great (score: 0.21)`
11130	`Total keywords: 9 extracted in 0.0192 seconds`
11131
11132	`Dependency Relations (extracted in 0.0057sec):`
11133
11134	`Sentence 1: BOUDIN:`
11135	`BOUDIN (PROPN) --[ROOT]--> BOUDIN (PROPN)`
11136	`: (PUNCT) --[punct]--> BOUDIN (PROPN)`
11137
11138	`Sentence 2: Thank you.`
11139	`Thank (VERB) --[ROOT]--> Thank (VERB)`
11140	`you (PRON) --[dobj]--> Thank (VERB)`
11141	`. (PUNCT) --[punct]--> Thank (VERB)`
11142
11143	`Sentence 3: Have a great day.`
11144	`Have (VERB) --[ROOT]--> Have (VERB)`
11145	`a (DET) --[det]--> day (NOUN)`
11146	`great (ADJ) --[amod]--> day (NOUN)`
11147	`day (NOUN) --[dobj]--> Have (VERB)`
11148	`. (PUNCT) --[punct]--> Have (VERB)`
11149
11150	`Total sentences: 3`
11151
11152	`RAKE Keyphrase Relationships:`
11153	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
11154
11155	`YAKE Keyphrase Relationships:`
11156	`noun phrase: "a great day" contains [great day], [day], [great]`
11157	`Total relationships found: 1`
11158
11159	`Timing Comparison:`
11160	`RAKE: 0.0000sec`
11161	`YAKE: 0.0000sec`
11162	`KeyBERT: 0.0192sec`
11163	`Dependencies: 0.0057sec`
11164	`Fastest: RAKE`
11165
11166	`================================================================================`
11167	`Message 82:`
11168	`CIRISANO: When you open TikTok, even if you've never used the app before and aren't following anyone, it opens immediately to a discovery page and just starts feeding you content. Like, you open it, and you're on the train.`
11169	`--------------------------------------------------------------------------------`
11170	`RAKE Keywords:`
11171	`- starts feeding (score: 4.00) → start feed`
11172	`- opens immediately (score: 4.00) → open immediately`
11173	`- never used (score: 4.00) → never use`
11174	`- following anyone (score: 4.00) → follow anyone`
11175	`- discovery page (score: 4.00)`
11176	`- open tiktok (score: 3.50) → open TikTok`
11177	`- open (score: 1.50)`
11178	`- train (score: 1.00)`
11179	`- like (score: 1.00)`
11180	`- even (score: 1.00)`
11181	`- content (score: 1.00)`
11182	`- cirisano (score: 1.00) → CIRISANO`
11183	`- app (score: 1.00)`
11184	`Total keywords: 13 extracted in 0.0000 seconds`
11185
11186	`YAKE Keywords:`
11187	`- feeding you content (score: 0.02) → feed you content`
11188	`- discovery page (score: 0.02)`
11189	`- starts feeding (score: 0.02) → start feed`
11190	`- open TikTok (score: 0.03) → open TikTok`
11191	`- opens immediately (score: 0.04) → open immediately`
11192	`- CIRISANO (score: 0.04) → CIRISANO`
11193	`- TikTok (score: 0.11) → TikTok`
11194	`- content (score: 0.11)`
11195	`- open (score: 0.12)`
11196	`- app (score: 0.15)`
11197	`- immediately (score: 0.15)`
11198	`- discovery (score: 0.15)`
11199	`- page (score: 0.15)`
11200	`- starts (score: 0.15) → start`
11201	`- feeding (score: 0.15) → feed`
11202	`- opens (score: 0.25) → open`
11203	`- train (score: 0.31)`
11204	`Total keywords: 17 extracted in 0.0097 seconds`
11205
11206	`KeyBERT Keywords:`
11207	`- cirisano open tiktok (score: 0.75)`
11208	`- open tiktok (score: 0.66)`
11209	`- tiktok (score: 0.60)`
11210	`- open tiktok ve (score: 0.57)`
11211	`- tiktok ve used (score: 0.54)`
11212	`- cirisano open (score: 0.50)`
11213	`- tiktok ve (score: 0.50)`
11214	`- opens immediately discovery (score: 0.48)`
11215	`- cirisano (score: 0.44)`
11216	`- app aren following (score: 0.43)`
11217	`- immediately discovery page (score: 0.42)`
11218	`- discovery page just (score: 0.39)`
11219	`- starts feeding content (score: 0.38)`
11220	`- discovery page (score: 0.38)`
11221	`- used app (score: 0.37)`
11222	`- app (score: 0.37)`
11223	`- following opens immediately (score: 0.35)`
11224	`- opens immediately (score: 0.35)`
11225	`- content like open (score: 0.34)`
11226	`- ve used app (score: 0.33)`
11227	`Total keywords: 20 extracted in 0.0443 seconds`
11228
11229	`Dependency Relations (extracted in 0.0057sec):`
11230
11231	`Sentence 1: CIRISANO: When you open TikTok, even if you've never used the app before and aren't following anyone, it opens immediately to a discovery page and just starts feeding you content.`
11232	`CIRISANO (PROPN) --[advcl]--> opens (VERB)`
11233	`: (PUNCT) --[punct]--> CIRISANO (PROPN)`
11234	`When (SCONJ) --[advmod]--> open (VERB)`
11235	`you (PRON) --[nsubj]--> open (VERB)`
11236	`open (VERB) --[advcl]--> CIRISANO (PROPN)`
11237	`TikTok (PROPN) --[dobj]--> open (VERB)`
11238	`, (PUNCT) --[punct]--> CIRISANO (PROPN)`
11239	`even (ADV) --[advmod]--> used (VERB)`
11240	`if (SCONJ) --[mark]--> used (VERB)`
11241	`you (PRON) --[nsubj]--> used (VERB)`
11242	`'ve (AUX) --[aux]--> used (VERB)`
11243	`never (ADV) --[neg]--> used (VERB)`
11244	`used (VERB) --[advcl]--> opens (VERB)`
11245	`the (DET) --[det]--> app (NOUN)`
11246	`app (NOUN) --[dobj]--> used (VERB)`
11247	`before (ADV) --[advmod]--> used (VERB)`
11248	`and (CCONJ) --[cc]--> used (VERB)`
11249	`are (AUX) --[aux]--> following (VERB)`
11250	`n't (PART) --[neg]--> following (VERB)`
11251	`following (VERB) --[conj]--> used (VERB)`
11252	`anyone (PRON) --[dobj]--> following (VERB)`
11253	`, (PUNCT) --[punct]--> opens (VERB)`
11254	`it (PRON) --[nsubj]--> opens (VERB)`
11255	`opens (VERB) --[ROOT]--> opens (VERB)`
11256	`immediately (ADV) --[advmod]--> opens (VERB)`
11257	`to (ADP) --[prep]--> opens (VERB)`
11258	`a (DET) --[det]--> page (NOUN)`
11259	`discovery (NOUN) --[compound]--> page (NOUN)`
11260	`page (NOUN) --[pobj]--> to (ADP)`
11261	`and (CCONJ) --[cc]--> opens (VERB)`
11262	`just (ADV) --[advmod]--> starts (VERB)`
11263	`starts (VERB) --[conj]--> opens (VERB)`
11264	`feeding (VERB) --[xcomp]--> starts (VERB)`
11265	`you (PRON) --[compound]--> content (NOUN)`
11266	`content (NOUN) --[dobj]--> feeding (VERB)`
11267	`. (PUNCT) --[punct]--> opens (VERB)`
11268
11269	`Sentence 2: Like, you open it, and you're on the train.`
11270	`Like (INTJ) --[intj]--> open (VERB)`
11271	`, (PUNCT) --[punct]--> open (VERB)`
11272	`you (PRON) --[nsubj]--> open (VERB)`
11273	`open (VERB) --[ROOT]--> open (VERB)`
11274	`it (PRON) --[dobj]--> open (VERB)`
11275	`, (PUNCT) --[punct]--> open (VERB)`
11276	`and (CCONJ) --[cc]--> open (VERB)`
11277	`you (PRON) --[nsubj]--> 're (AUX)`
11278	`'re (AUX) --[conj]--> open (VERB)`
11279	`on (ADP) --[prep]--> 're (AUX)`
11280	`the (DET) --[det]--> train (NOUN)`
11281	`train (NOUN) --[pobj]--> on (ADP)`
11282	`. (PUNCT) --[punct]--> 're (AUX)`
11283
11284	`Total sentences: 2`
11285
11286	`RAKE Keyphrase Relationships:`
11287	`verb phrase: "used even 've never app before" contains [even], [app]`
11288	`verb phrase: "opens immediately to" contains [opens immediately], [open]`
11289	`Total relationships found: 2`
11290
11291	`YAKE Keyphrase Relationships:`
11292	`noun phrase: "a discovery page" contains [discovery page], [discovery], [page]`
11293	`verb phrase: "open When TikTok" contains [tiktok], [open]`
11294	`verb phrase: "opens immediately to" contains [opens immediately], [open], [immediately], [opens]`
11295	`verb phrase: "feeding content" contains [content], [feeding]`
11296	`Total relationships found: 4`
11297
11298	`Timing Comparison:`
11299	`RAKE: 0.0000sec`
11300	`YAKE: 0.0097sec`
11301	`KeyBERT: 0.0443sec`
11302	`Dependencies: 0.0057sec`
11303	`Fastest: RAKE`
11304
11305	`================================================================================`
11306	`Message 83:`
11307	ROSE: Well, exactly. This is very unusual. The public and reporters are rarely allowed into these places. But the allegations here are so serious that the Border Patrol says they have to defend themselves. I mean, understand these allegations have made headlines for days. The head of Customs and Border Protection announced his resignation after the story broke. Even President Trump and Vice President Pence have been expressing concern for the migrant kids held here. So while CBP denied the allegations, it became sort of a he said, she said story. And reporters were asking who they should believe, and they asked to see the facility for themselves. Yesterday, CBP said they didn't have time to take anyone on a tour, that they needed all hands on deck to process the, you know, influx of migrants crossing the border. But today, apparently, they changed their mind.
11308	`--------------------------------------------------------------------------------`
11309	`RAKE Keywords:`
11310	`- vice president pence (score: 9.00) → Vice President Pence`
11311	`- migrant kids held (score: 9.00) → migrant kid hold`
11312	`- even president trump (score: 9.00) → even President Trump`
11313	`- border protection announced (score: 8.33) → Border Protection announce`
11314	`- border patrol says (score: 8.33) → Border Patrol say`
11315	`- take anyone (score: 4.00)`
11316	`- story broke (score: 4.00) → story break`
11317	`- rarely allowed (score: 4.00) → rarely allow`
11318	`- migrants crossing (score: 4.00) → migrant cross`
11319	`- made headlines (score: 4.00) → make headline`
11320	`- expressing concern (score: 4.00) → express concern`
11321	`- cbp denied (score: 4.00) → CBP deny`
11322	`- became sort (score: 4.00) → become sort`
11323	`- said story (score: 3.67) → say story`
11324	`- cbp said (score: 3.67) → CBP say`
11325	`- border (score: 2.33) → Border`
11326	`- said (score: 1.67) → say`
11327	`- yesterday (score: 1.00)`
11328	`- well (score: 1.00)`
11329	`- unusual (score: 1.00)`
11330	`- understand (score: 1.00)`
11331	`- tour (score: 1.00)`
11332	`- today (score: 1.00)`
11333	`- time (score: 1.00)`
11334	`- serious (score: 1.00)`
11335	`- see (score: 1.00)`
11336	`- rose (score: 1.00) → ROSE`
11337	`- resignation (score: 1.00)`
11338	`- reporters (score: 1.00) → reporter`
11339	`- reporters (score: 1.00) → reporter`
11340	`- public (score: 1.00)`
11341	`- process (score: 1.00)`
11342	`- places (score: 1.00) → place`
11343	`- needed (score: 1.00) → need`
11344	`- mind (score: 1.00)`
11345	`- mean (score: 1.00)`
11346	`- know (score: 1.00)`
11347	`- influx (score: 1.00)`
11348	`- head (score: 1.00)`
11349	`- hands (score: 1.00) → hand`
11350	`- facility (score: 1.00)`
11351	`- exactly (score: 1.00)`
11352	`- defend (score: 1.00)`
11353	`- deck (score: 1.00)`
11354	`- days (score: 1.00) → day`
11355	`- customs (score: 1.00) → Customs`
11356	`- changed (score: 1.00) → change`
11357	`- believe (score: 1.00)`
11358	`- asking (score: 1.00) → ask`
11359	`- asked (score: 1.00) → ask`
11360	`- apparently (score: 1.00)`
11361	`- allegations (score: 1.00) → allegation`
11362	`- allegations (score: 1.00) → allegation`
11363	`- allegations (score: 1.00) → allegation`
11364	`Total keywords: 54 extracted in 0.0000 seconds`
11365
11366	`YAKE Keywords:`
11367	`- ROSE (score: 0.05) → ROSE`
11368	`- Vice President Pence (score: 0.07) → Vice President Pence`
11369	`- Border (score: 0.11) → Border`
11370	`- Border Patrol (score: 0.11) → Border Patrol`
11371	`- Border Protection announced (score: 0.11) → Border Protection announce`
11372	`- Border Protection (score: 0.13) → Border Protection`
11373	`- allegations (score: 0.14) → allegation`
11374	`- President Trump (score: 0.15) → President Trump`
11375	`- Vice President (score: 0.15) → Vice President`
11376	`- President Pence (score: 0.15) → President Pence`
11377	`- Trump and Vice (score: 0.16) → Trump and Vice`
11378	`- President (score: 0.17) → President`
11379	`- CBP (score: 0.18) → CBP`
11380	`- reporters (score: 0.24) → reporter`
11381	`- story (score: 0.26)`
11382	`- head of Customs (score: 0.28) → head of Customs`
11383	`- Protection announced (score: 0.28) → Protection announce`
11384	`- CBP denied (score: 0.30) → CBP deny`
11385	`- public and reporters (score: 0.31) → public and reporter`
11386	`- Patrol (score: 0.31) → Patrol`
11387	`Total keywords: 20 extracted in 0.0230 seconds`
11388
11389	`KeyBERT Keywords:`
11390	`- allegations border patrol (score: 0.63)`
11391	`- allegations border (score: 0.57)`
11392	`- places allegations border (score: 0.55)`
11393	`- cbp denied allegations (score: 0.52)`
11394	`- border patrol says (score: 0.51)`
11395	`- border protection announced (score: 0.51)`
11396	`- understand allegations headlines (score: 0.46)`
11397	`- border protection (score: 0.44)`
11398	`- migrants crossing border (score: 0.44)`
11399	`- border patrol (score: 0.43)`
11400	`- cbp said (score: 0.43)`
11401	`- yesterday cbp said (score: 0.42)`
11402	`- held cbp denied (score: 0.42)`
11403	`- understand allegations (score: 0.42)`
11404	`- border today apparently (score: 0.42)`
11405	`- cbp said didn (score: 0.42)`
11406	`- patrol says defend (score: 0.42)`
11407	`- cbp denied (score: 0.41)`
11408	`- crossing border today (score: 0.40)`
11409	`- border today (score: 0.40)`
11410	`Total keywords: 20 extracted in 0.1027 seconds`
11411
11412	`Dependency Relations (extracted in 0.0175sec):`
11413
11414	`Sentence 1: ROSE:`
11415	`ROSE (PROPN) --[ROOT]--> ROSE (PROPN)`
11416	`: (PUNCT) --[punct]--> ROSE (PROPN)`
11417
11418	`Sentence 2: Well, exactly.`
11419	`Well (INTJ) --[intj]--> exactly (ADV)`
11420	`, (PUNCT) --[punct]--> exactly (ADV)`
11421	`exactly (ADV) --[ROOT]--> exactly (ADV)`
11422	`. (PUNCT) --[punct]--> exactly (ADV)`
11423
11424	`Sentence 3: This is very unusual.`
11425	`This (PRON) --[nsubj]--> is (AUX)`
11426	`is (AUX) --[ROOT]--> is (AUX)`
11427	`very (ADV) --[advmod]--> unusual (ADJ)`
11428	`unusual (ADJ) --[acomp]--> is (AUX)`
11429	`. (PUNCT) --[punct]--> is (AUX)`
11430
11431	`Sentence 4: The public and reporters are rarely allowed into these places.`
11432	`The (DET) --[det]--> public (NOUN)`
11433	`public (NOUN) --[nsubjpass]--> allowed (VERB)`
11434	`and (CCONJ) --[cc]--> public (NOUN)`
11435	`reporters (NOUN) --[conj]--> public (NOUN)`
11436	`are (AUX) --[auxpass]--> allowed (VERB)`
11437	`rarely (ADV) --[advmod]--> allowed (VERB)`
11438	`allowed (VERB) --[ROOT]--> allowed (VERB)`
11439	`into (ADP) --[prep]--> allowed (VERB)`
11440	`these (DET) --[det]--> places (NOUN)`
11441	`places (NOUN) --[pobj]--> into (ADP)`
11442	`. (PUNCT) --[punct]--> allowed (VERB)`
11443
11444	`Sentence 5: But the allegations here are so serious that the Border Patrol says they have to defend themselves.`
11445	`But (CCONJ) --[cc]--> are (AUX)`
11446	`the (DET) --[det]--> allegations (NOUN)`
11447	`allegations (NOUN) --[nsubj]--> are (AUX)`
11448	`here (ADV) --[advmod]--> allegations (NOUN)`
11449	`are (AUX) --[ROOT]--> are (AUX)`
11450	`so (ADV) --[advmod]--> serious (ADJ)`
11451	`serious (ADJ) --[acomp]--> are (AUX)`
11452	`that (SCONJ) --[mark]--> says (VERB)`
11453	`the (DET) --[det]--> Patrol (PROPN)`
11454	`Border (PROPN) --[compound]--> Patrol (PROPN)`
11455	`Patrol (PROPN) --[nsubj]--> says (VERB)`
11456	`says (VERB) --[ccomp]--> serious (ADJ)`
11457	`they (PRON) --[nsubj]--> have (VERB)`
11458	`have (VERB) --[ccomp]--> says (VERB)`
11459	`to (PART) --[aux]--> defend (VERB)`
11460	`defend (VERB) --[xcomp]--> have (VERB)`
11461	`themselves (PRON) --[dobj]--> defend (VERB)`
11462	`. (PUNCT) --[punct]--> are (AUX)`
11463
11464	`Sentence 6: I mean, understand these allegations have made headlines for days.`
11465	`I (PRON) --[nsubj]--> mean (VERB)`
11466	`mean (VERB) --[parataxis]--> understand (VERB)`
11467	`, (PUNCT) --[punct]--> understand (VERB)`
11468	`understand (VERB) --[ROOT]--> understand (VERB)`
11469	`these (DET) --[det]--> allegations (NOUN)`
11470	`allegations (NOUN) --[nsubj]--> made (VERB)`
11471	`have (AUX) --[aux]--> made (VERB)`
11472	`made (VERB) --[ccomp]--> understand (VERB)`
11473	`headlines (NOUN) --[dobj]--> made (VERB)`
11474	`for (ADP) --[prep]--> made (VERB)`
11475	`days (NOUN) --[pobj]--> for (ADP)`
11476	`. (PUNCT) --[punct]--> understand (VERB)`
11477
11478	`Sentence 7: The head of Customs and Border Protection announced his resignation after the story broke.`
11479	`The (DET) --[det]--> head (NOUN)`
11480	`head (NOUN) --[nsubj]--> announced (VERB)`
11481	`of (ADP) --[prep]--> head (NOUN)`
11482	`Customs (PROPN) --[pobj]--> of (ADP)`
11483	`and (CCONJ) --[cc]--> Customs (PROPN)`
11484	`Border (PROPN) --[compound]--> Protection (PROPN)`
11485	`Protection (PROPN) --[conj]--> Customs (PROPN)`
11486	`announced (VERB) --[ROOT]--> announced (VERB)`
11487	`his (PRON) --[poss]--> resignation (NOUN)`
11488	`resignation (NOUN) --[dobj]--> announced (VERB)`
11489	`after (SCONJ) --[mark]--> broke (VERB)`
11490	`the (DET) --[det]--> story (NOUN)`
11491	`story (NOUN) --[nsubj]--> broke (VERB)`
11492	`broke (VERB) --[advcl]--> announced (VERB)`
11493	`. (PUNCT) --[punct]--> announced (VERB)`
11494
11495	`Sentence 8: Even President Trump and Vice President Pence have been expressing concern for the migrant kids held here.`
11496	`Even (ADV) --[advmod]--> President (PROPN)`
11497	`President (PROPN) --[compound]--> Trump (PROPN)`
11498	`Trump (PROPN) --[nsubj]--> expressing (VERB)`
11499	`and (CCONJ) --[cc]--> Trump (PROPN)`
11500	`Vice (PROPN) --[compound]--> President (PROPN)`
11501	`President (PROPN) --[compound]--> Pence (PROPN)`
11502	`Pence (PROPN) --[conj]--> Trump (PROPN)`
11503	`have (AUX) --[aux]--> expressing (VERB)`
11504	`been (AUX) --[aux]--> expressing (VERB)`
11505	`expressing (VERB) --[ROOT]--> expressing (VERB)`
11506	`concern (NOUN) --[dobj]--> expressing (VERB)`
11507	`for (ADP) --[prep]--> concern (NOUN)`
11508	`the (DET) --[det]--> kids (NOUN)`
11509	`migrant (ADJ) --[amod]--> kids (NOUN)`
11510	`kids (NOUN) --[pobj]--> for (ADP)`
11511	`held (VERB) --[acl]--> kids (NOUN)`
11512	`here (ADV) --[advmod]--> held (VERB)`
11513	`. (PUNCT) --[punct]--> expressing (VERB)`
11514
11515	`Sentence 9: So while CBP denied the allegations, it became sort of a he said, she said story.`
11516	`So (ADV) --[advmod]--> became (VERB)`
11517	`while (SCONJ) --[mark]--> denied (VERB)`
11518	`CBP (PROPN) --[nsubj]--> denied (VERB)`
11519	`denied (VERB) --[advcl]--> became (VERB)`
11520	`the (DET) --[det]--> allegations (NOUN)`
11521	`allegations (NOUN) --[dobj]--> denied (VERB)`
11522	`, (PUNCT) --[punct]--> became (VERB)`
11523	`it (PRON) --[nsubj]--> became (VERB)`
11524	`became (VERB) --[ROOT]--> became (VERB)`
11525	`sort (ADV) --[advmod]--> of (ADV)`
11526	`of (ADV) --[advmod]--> a (PRON)`
11527	`a (PRON) --[attr]--> became (VERB)`
11528	`he (PRON) --[nsubj]--> said (VERB)`
11529	`said (VERB) --[parataxis]--> became (VERB)`
11530	`, (PUNCT) --[punct]--> said (VERB)`
11531	`she (PRON) --[nsubj]--> said (VERB)`
11532	`said (VERB) --[ccomp]--> said (VERB)`
11533	`story (NOUN) --[dobj]--> said (VERB)`
11534	`. (PUNCT) --[punct]--> said (VERB)`
11535
11536	`Sentence 10: And reporters were asking who they should believe, and they asked to see the facility for themselves.`
11537	`And (CCONJ) --[cc]--> asking (VERB)`
11538	`reporters (NOUN) --[nsubj]--> asking (VERB)`
11539	`were (AUX) --[aux]--> asking (VERB)`
11540	`asking (VERB) --[ROOT]--> asking (VERB)`
11541	`who (PRON) --[dobj]--> believe (VERB)`
11542	`they (PRON) --[nsubj]--> believe (VERB)`
11543	`should (AUX) --[aux]--> believe (VERB)`
11544	`believe (VERB) --[ccomp]--> asking (VERB)`
11545	`, (PUNCT) --[punct]--> asking (VERB)`
11546	`and (CCONJ) --[cc]--> asking (VERB)`
11547	`they (PRON) --[nsubj]--> asked (VERB)`
11548	`asked (VERB) --[conj]--> asking (VERB)`
11549	`to (PART) --[aux]--> see (VERB)`
11550	`see (VERB) --[xcomp]--> asked (VERB)`
11551	`the (DET) --[det]--> facility (NOUN)`
11552	`facility (NOUN) --[dobj]--> see (VERB)`
11553	`for (ADP) --[prep]--> see (VERB)`
11554	`themselves (PRON) --[pobj]--> for (ADP)`
11555	`. (PUNCT) --[punct]--> asked (VERB)`
11556
11557	`Sentence 11: Yesterday, CBP said they didn't have time to take anyone on a tour, that they needed all hands on deck to process the, you know, influx of migrants crossing the border.`
11558	`Yesterday (NOUN) --[npadvmod]--> said (VERB)`
11559	`, (PUNCT) --[punct]--> said (VERB)`
11560	`CBP (PROPN) --[nsubj]--> said (VERB)`
11561	`said (VERB) --[ROOT]--> said (VERB)`
11562	`they (PRON) --[nsubj]--> have (VERB)`
11563	`did (AUX) --[aux]--> have (VERB)`
11564	`n't (PART) --[neg]--> have (VERB)`
11565	`have (VERB) --[ccomp]--> said (VERB)`
11566	`time (NOUN) --[dobj]--> have (VERB)`
11567	`to (PART) --[aux]--> take (VERB)`
11568	`take (VERB) --[relcl]--> time (NOUN)`
11569	`anyone (PRON) --[dobj]--> take (VERB)`
11570	`on (ADP) --[prep]--> take (VERB)`
11571	`a (DET) --[det]--> tour (NOUN)`
11572	`tour (NOUN) --[pobj]--> on (ADP)`
11573	`, (PUNCT) --[punct]--> have (VERB)`
11574	`that (SCONJ) --[mark]--> needed (VERB)`
11575	`they (PRON) --[nsubj]--> needed (VERB)`
11576	`needed (VERB) --[ccomp]--> said (VERB)`
11577	`all (DET) --[det]--> hands (NOUN)`
11578	`hands (NOUN) --[dobj]--> needed (VERB)`
11579	`on (ADP) --[prep]--> needed (VERB)`
11580	`deck (NOUN) --[pobj]--> on (ADP)`
11581	`to (PART) --[aux]--> process (VERB)`
11582	`process (VERB) --[advcl]--> needed (VERB)`
11583	`the (DET) --[det]--> influx (NOUN)`
11584	`, (PUNCT) --[punct]--> know (VERB)`
11585	`you (PRON) --[nsubj]--> know (VERB)`
11586	`know (VERB) --[parataxis]--> influx (NOUN)`
11587	`, (PUNCT) --[punct]--> know (VERB)`
11588	`influx (NOUN) --[dobj]--> process (VERB)`
11589	`of (ADP) --[prep]--> influx (NOUN)`
11590	`migrants (NOUN) --[pobj]--> of (ADP)`
11591	`crossing (VERB) --[acl]--> migrants (NOUN)`
11592	`the (DET) --[det]--> border (NOUN)`
11593	`border (NOUN) --[dobj]--> crossing (VERB)`
11594	`. (PUNCT) --[punct]--> said (VERB)`
11595
11596	`Sentence 12: But today, apparently, they changed their mind.`
11597	`But (CCONJ) --[cc]--> changed (VERB)`
11598	`today (NOUN) --[npadvmod]--> changed (VERB)`
11599	`, (PUNCT) --[punct]--> changed (VERB)`
11600	`apparently (ADV) --[advmod]--> changed (VERB)`
11601	`, (PUNCT) --[punct]--> changed (VERB)`
11602	`they (PRON) --[nsubj]--> changed (VERB)`
11603	`changed (VERB) --[ROOT]--> changed (VERB)`
11604	`their (PRON) --[poss]--> mind (NOUN)`
11605	`mind (NOUN) --[dobj]--> changed (VERB)`
11606	`. (PUNCT) --[punct]--> changed (VERB)`
11607
11608	`Total sentences: 12`
11609
11610	`RAKE Keyphrase Relationships:`
11611	`noun phrase: "reporters" contains [reporters], [reporters]`
11612	`noun phrase: "the allegations" contains [allegations], [allegations], [allegations]`
11613	`noun phrase: "these allegations" contains [allegations], [allegations], [allegations]`
11614	`noun phrase: "the allegations" contains [allegations], [allegations], [allegations]`
11615	`noun phrase: "reporters" contains [reporters], [reporters]`
11616	`verb phrase: "denied allegations" contains [allegations], [allegations], [allegations]`
11617	`verb phrase: "said story" contains [said story], [said]`
11618	`verb phrase: "see to facility for" contains [see], [facility]`
11619	`verb phrase: "needed hands on" contains [needed], [hands]`
11620	`verb phrase: "process to influx" contains [process], [influx]`
11621	`verb phrase: "changed apparently mind" contains [mind], [changed], [apparently]`
11622	`Total relationships found: 11`
11623
11624	`YAKE Keyphrase Relationships:`
11625	`noun phrase: "the Border Patrol" contains [border], [border patrol], [patrol]`
11626	`noun phrase: "Border Protection" contains [border], [border protection]`
11627	`noun phrase: "Even President Trump" contains [president trump], [president]`
11628	`noun phrase: "Vice President Pence" contains [vice president pence], [vice president], [president pence], [president]`
11629	`Total relationships found: 4`
11630
11631	`Timing Comparison:`
11632	`RAKE: 0.0000sec`
11633	`YAKE: 0.0230sec`
11634	`KeyBERT: 0.1027sec`
11635	`Dependencies: 0.0175sec`
11636	`Fastest: RAKE`
11637
11638	`================================================================================`
11639	`Message 84:`
11640	`KELLY: To the role that Amy Coney Barrett could play, she's been on the court a little over a year now. Overall, what kind of justice has she been?`
11641	`--------------------------------------------------------------------------------`
11642	`RAKE Keywords:`
11643	`- year (score: 1.00)`
11644	`- role (score: 1.00)`
11645	`- overall (score: 1.00)`
11646	`- little (score: 1.00)`
11647	`- kind (score: 1.00)`
11648	`- kelly (score: 1.00) → KELLY`
11649	`- justice (score: 1.00)`
11650	`- court (score: 1.00)`
11651	`Total keywords: 8 extracted in 0.0000 seconds`
11652
11653	`YAKE Keywords:`
11654	`- Amy Coney Barrett (score: 0.00) → Amy Coney Barrett`
11655	`- Amy Coney (score: 0.01) → Amy Coney`
11656	`- Coney Barrett (score: 0.01) → Coney Barrett`
11657	`- Barrett could play (score: 0.01) → Barrett could play`
11658	`- role that Amy (score: 0.02) → role that Amy`
11659	`- KELLY (score: 0.04) → KELLY`
11660	`- Amy (score: 0.10) → Amy`
11661	`- Coney (score: 0.10) → Coney`
11662	`- Barrett (score: 0.10) → Barrett`
11663	`- play (score: 0.12)`
11664	`- role (score: 0.20)`
11665	`- court (score: 0.20)`
11666	`- year (score: 0.20)`
11667	`- kind of justice (score: 0.28)`
11668	`- kind (score: 0.47)`
11669	`- justice (score: 0.47)`
11670	`Total keywords: 16 extracted in 0.0080 seconds`
11671
11672	`KeyBERT Keywords:`
11673	`- amy coney barrett (score: 0.59)`
11674	`- kelly role (score: 0.57)`
11675	`- kelly role amy (score: 0.56)`
11676	`- kelly (score: 0.53)`
11677	`- barrett play court (score: 0.52)`
11678	`- role amy coney (score: 0.49)`
11679	`- coney barrett (score: 0.48)`
11680	`- coney barrett play (score: 0.48)`
11681	`- overall kind justice (score: 0.45)`
11682	`- amy coney (score: 0.44)`
11683	`- justice (score: 0.44)`
11684	`- barrett play (score: 0.42)`
11685	`- barrett (score: 0.41)`
11686	`- kind justice (score: 0.40)`
11687	`- role amy (score: 0.39)`
11688	`- court (score: 0.38)`
11689	`- court little (score: 0.35)`
11690	`- amy (score: 0.35)`
11691	`- court little year (score: 0.34)`
11692	`- play court (score: 0.33)`
11693	`Total keywords: 20 extracted in 0.0344 seconds`
11694
11695	`Dependency Relations (extracted in 0.0040sec):`
11696
11697	`Sentence 1: KELLY:`
11698	`KELLY (PROPN) --[ROOT]--> KELLY (PROPN)`
11699	`: (PUNCT) --[punct]--> KELLY (PROPN)`
11700
11701	`Sentence 2: To the role that Amy Coney Barrett could play, she's been on the court a little over a year now.`
11702	`To (ADP) --[prep]--> been (AUX)`
11703	`the (DET) --[det]--> role (NOUN)`
11704	`role (NOUN) --[pobj]--> To (ADP)`
11705	`that (PRON) --[dobj]--> play (VERB)`
11706	`Amy (PROPN) --[compound]--> Barrett (PROPN)`
11707	`Coney (PROPN) --[compound]--> Barrett (PROPN)`
11708	`Barrett (PROPN) --[nsubj]--> play (VERB)`
11709	`could (AUX) --[aux]--> play (VERB)`
11710	`play (VERB) --[relcl]--> role (NOUN)`
11711	`, (PUNCT) --[punct]--> been (AUX)`
11712	`she (PRON) --[nsubjpass]--> been (AUX)`
11713	`'s (AUX) --[auxpass]--> been (AUX)`
11714	`been (AUX) --[ROOT]--> been (AUX)`
11715	`on (ADP) --[prep]--> been (AUX)`
11716	`the (DET) --[det]--> court (NOUN)`
11717	`court (NOUN) --[pobj]--> on (ADP)`
11718	`a (DET) --[det]--> little (ADJ)`
11719	`little (ADJ) --[amod]--> year (NOUN)`
11720	`over (ADP) --[quantmod]--> a (PRON)`
11721	`a (PRON) --[nummod]--> year (NOUN)`
11722	`year (NOUN) --[npadvmod]--> now (ADV)`
11723	`now (ADV) --[advmod]--> been (AUX)`
11724	`. (PUNCT) --[punct]--> been (AUX)`
11725
11726	`Sentence 3: Overall, what kind of justice has she been?`
11727	`Overall (ADV) --[advmod]--> been (AUX)`
11728	`, (PUNCT) --[punct]--> been (AUX)`
11729	`what (DET) --[det]--> kind (NOUN)`
11730	`kind (NOUN) --[attr]--> been (AUX)`
11731	`of (ADP) --[prep]--> kind (NOUN)`
11732	`justice (NOUN) --[pobj]--> of (ADP)`
11733	`has (AUX) --[aux]--> been (AUX)`
11734	`she (PRON) --[nsubj]--> been (AUX)`
11735	`been (AUX) --[ROOT]--> been (AUX)`
11736	`? (PUNCT) --[punct]--> been (AUX)`
11737
11738	`Total sentences: 3`
11739
11740	`RAKE Keyphrase Relationships:`
11741	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
11742
11743	`YAKE Keyphrase Relationships:`
11744	`noun phrase: "Amy Coney Barrett" contains [amy coney barrett], [amy coney], [coney barrett], [amy], [coney], [barrett]`
11745	`Total relationships found: 1`
11746
11747	`Timing Comparison:`
11748	`RAKE: 0.0000sec`
11749	`YAKE: 0.0080sec`
11750	`KeyBERT: 0.0344sec`
11751	`Dependencies: 0.0040sec`
11752	`Fastest: RAKE`
11753
11754	`================================================================================`
11755	`Message 85:`
11756	`SHAPIRO: You document a protest movement that has emerged from this incident, and there is a scene of people holding signs and shouting, they took them alive, and alive we want them back.(SOUNDBITE OF ARCHIVED RECORDING)`
11757	`--------------------------------------------------------------------------------`
11758	`RAKE Keywords:`
11759	`- people holding signs (score: 9.00) → people hold sign`
11760	`- protest movement (score: 4.00)`
11761	`- archived recording (score: 4.00)`
11762	`- want (score: 1.00)`
11763	`- took (score: 1.00) → take`
11764	`- soundbite (score: 1.00)`
11765	`- shouting (score: 1.00) → shout`
11766	`- shapiro (score: 1.00) → SHAPIRO`
11767	`- scene (score: 1.00)`
11768	`- incident (score: 1.00)`
11769	`- emerged (score: 1.00) → emerge`
11770	`- document (score: 1.00)`
11771	`- back (score: 1.00)`
11772	`- alive (score: 1.00)`
11773	`- alive (score: 1.00)`
11774	`Total keywords: 15 extracted in 0.0000 seconds`
11775
11776	`YAKE Keywords:`
11777	`- people holding signs (score: 0.00) → people hold sign`
11778	`- SOUNDBITE OF ARCHIVED (score: 0.00)`
11779	`- ARCHIVED RECORDING (score: 0.00)`
11780	`- signs and shouting (score: 0.01) → sign and shout`
11781	`- document a protest (score: 0.02)`
11782	`- protest movement (score: 0.02)`
11783	`- scene of people (score: 0.02)`
11784	`- people holding (score: 0.02) → people hold`
11785	`- holding signs (score: 0.02) → hold sign`
11786	`- SHAPIRO (score: 0.03) → SHAPIRO`
11787	`- SOUNDBITE (score: 0.05)`
11788	`- RECORDING (score: 0.05)`
11789	`- ARCHIVED (score: 0.07)`
11790	`- alive (score: 0.08)`
11791	`- incident (score: 0.09)`
11792	`- shouting (score: 0.09) → shout`
11793	`- back. (score: 0.09)`
11794	`- document (score: 0.13)`
11795	`- protest (score: 0.13)`
11796	`- movement (score: 0.13)`
11797	`Total keywords: 20 extracted in 0.0135 seconds`
11798
11799	`KeyBERT Keywords:`
11800	`- shapiro document protest (score: 0.73)`
11801	`- document protest movement (score: 0.62)`
11802	`- document protest (score: 0.60)`
11803	`- protest movement (score: 0.59)`
11804	`- protest movement emerged (score: 0.58)`
11805	`- protest (score: 0.57)`
11806	`- shapiro document (score: 0.52)`
11807	`- holding signs shouting (score: 0.50)`
11808	`- signs shouting took (score: 0.49)`
11809	`- shouting took alive (score: 0.49)`
11810	`- shapiro (score: 0.46)`
11811	`- movement emerged incident (score: 0.44)`
11812	`- signs shouting (score: 0.44)`
11813	`- people holding signs (score: 0.40)`
11814	`- holding signs (score: 0.39)`
11815	`- took alive alive (score: 0.39)`
11816	`- took alive (score: 0.38)`
11817	`- shouting took (score: 0.37)`
11818	`- shouting (score: 0.36)`
11819	`- incident scene (score: 0.33)`
11820	`Total keywords: 20 extracted in 0.0411 seconds`
11821
11822	`Dependency Relations (extracted in 0.0100sec):`
11823
11824	`Sentence 1: SHAPIRO:`
11825	`SHAPIRO (PROPN) --[ROOT]--> SHAPIRO (PROPN)`
11826	`: (PUNCT) --[punct]--> SHAPIRO (PROPN)`
11827
11828	`Sentence 2: You document a protest movement that has emerged from this incident, and there is a scene of people holding signs and shouting, they took them alive, and alive we want them back.(SOUNDBITE OF ARCHIVED RECORDING)`
11829	`You (PRON) --[nsubj]--> document (VERB)`
11830	`document (VERB) --[ROOT]--> document (VERB)`
11831	`a (DET) --[det]--> movement (NOUN)`
11832	`protest (NOUN) --[compound]--> movement (NOUN)`
11833	`movement (NOUN) --[dobj]--> document (VERB)`
11834	`that (PRON) --[nsubj]--> emerged (VERB)`
11835	`has (AUX) --[aux]--> emerged (VERB)`
11836	`emerged (VERB) --[relcl]--> movement (NOUN)`
11837	`from (ADP) --[prep]--> emerged (VERB)`
11838	`this (DET) --[det]--> incident (NOUN)`
11839	`incident (NOUN) --[pobj]--> from (ADP)`
11840	`, (PUNCT) --[punct]--> document (VERB)`
11841	`and (CCONJ) --[cc]--> document (VERB)`
11842	`there (PRON) --[expl]--> is (VERB)`
11843	`is (VERB) --[conj]--> document (VERB)`
11844	`a (DET) --[det]--> scene (NOUN)`
11845	`scene (NOUN) --[attr]--> is (VERB)`
11846	`of (ADP) --[prep]--> scene (NOUN)`
11847	`people (NOUN) --[nsubj]--> holding (VERB)`
11848	`holding (VERB) --[pcomp]--> of (ADP)`
11849	`signs (NOUN) --[dobj]--> holding (VERB)`
11850	`and (CCONJ) --[cc]--> holding (VERB)`
11851	`shouting (VERB) --[conj]--> holding (VERB)`
11852	`, (PUNCT) --[punct]--> took (VERB)`
11853	`they (PRON) --[nsubj]--> took (VERB)`
11854	`took (VERB) --[conj]--> document (VERB)`
11855	`them (PRON) --[dobj]--> took (VERB)`
11856	`alive (ADJ) --[oprd]--> took (VERB)`
11857	`, (PUNCT) --[punct]--> took (VERB)`
11858	`and (CCONJ) --[cc]--> took (VERB)`
11859	`alive (ADJ) --[amod]--> want (VERB)`
11860	`we (PRON) --[nsubj]--> want (VERB)`
11861	`want (VERB) --[conj]--> took (VERB)`
11862	`them (PRON) --[nsubj]--> back.(SOUNDBITE (NOUN)`
11863	`back.(SOUNDBITE (NOUN) --[ccomp]--> want (VERB)`
11864	`OF (ADP) --[prep]--> back.(SOUNDBITE (NOUN)`
11865	`ARCHIVED (ADJ) --[amod]--> RECORDING (NOUN)`
11866	`RECORDING (NOUN) --[pobj]--> OF (ADP)`
11867	`) (PUNCT) --[punct]--> want (VERB)`
11868
11869	`Total sentences: 2`
11870
11871	`RAKE Keyphrase Relationships:`
11872	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
11873
11874	`YAKE Keyphrase Relationships:`
11875	`noun phrase: "a protest movement" contains [protest movement], [protest], [movement]`
11876	`noun phrase: "ARCHIVED RECORDING" contains [archived recording], [recording], [archived]`
11877	`verb phrase: "document movement" contains [document], [movement]`
11878	`Total relationships found: 3`
11879
11880	`Timing Comparison:`
11881	`RAKE: 0.0000sec`
11882	`YAKE: 0.0135sec`
11883	`KeyBERT: 0.0411sec`
11884	`Dependencies: 0.0100sec`
11885	`Fastest: RAKE`
11886
11887	`================================================================================`
11888	`Message 86:`
11889	`RAMBO: LAST BLOOD")`
11890	`--------------------------------------------------------------------------------`
11891	`RAKE Keywords:`
11892	`- last blood ") (score: 9.00)`
11893	`- rambo (score: 1.00) → RAMBO`
11894	`Total keywords: 2 extracted in 0.0000 seconds`
11895
11896	`YAKE Keywords:`
11897	`- RAMBO (score: 0.03) → RAMBO`
11898	`- BLOOD (score: 0.09)`
11899	`Total keywords: 2 extracted in 0.0000 seconds`
11900
11901	`KeyBERT Keywords:`
11902	`- rambo blood (score: 0.82)`
11903	`- rambo (score: 0.70)`
11904	`- blood (score: 0.48)`
11905	`Total keywords: 3 extracted in 0.0135 seconds`
11906
11907	`Dependency Relations (extracted in 0.0020sec):`
11908
11909	`Sentence 1: RAMBO: LAST BLOOD")`
11910	`RAMBO (PROPN) --[ROOT]--> RAMBO (PROPN)`
11911	`: (PUNCT) --[punct]--> RAMBO (PROPN)`
11912	`LAST (ADJ) --[amod]--> BLOOD (NOUN)`
11913	`BLOOD (NOUN) --[appos]--> RAMBO (PROPN)`
11914	`" (PUNCT) --[punct]--> RAMBO (PROPN)`
11915	`) (PUNCT) --[punct]--> RAMBO (PROPN)`
11916
11917	`Total sentences: 1`
11918
11919	`RAKE Keyphrase Relationships:`
11920	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
11921
11922	`YAKE Keyphrase Relationships:`
11923	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
11924
11925	`Timing Comparison:`
11926	`RAKE: 0.0000sec`
11927	`YAKE: 0.0000sec`
11928	`KeyBERT: 0.0135sec`
11929	`Dependencies: 0.0020sec`
11930	`Fastest: RAKE`
11931
11932	`================================================================================`
11933	`Message 87:`
11934	`BROCK SLABACH: We're already behind the eight ball because we have higher poverty, lower rates of education and less access to quality health care. And now through the pandemic, we've added this issue of mistrust in the very professionals that, for centuries, we've valued as an important source of information for health and health care.`
11935	`--------------------------------------------------------------------------------`
11936	`RAKE Keywords:`
11937	`- quality health care (score: 7.50)`
11938	`- health care (score: 4.50)`
11939	`- lower rates (score: 4.00) → low rate`
11940	`- less access (score: 4.00)`
11941	`- important source (score: 4.00)`
11942	`- higher poverty (score: 4.00) → high poverty`
11943	`- eight ball (score: 4.00)`
11944	`- brock slabach (score: 4.00) → BROCK SLABACH`
11945	`- already behind (score: 4.00)`
11946	`- health (score: 2.00)`
11947	`- valued (score: 1.00) → value`
11948	`- professionals (score: 1.00) → professional`
11949	`- pandemic (score: 1.00)`
11950	`- mistrust (score: 1.00)`
11951	`- issue (score: 1.00)`
11952	`- information (score: 1.00)`
11953	`- education (score: 1.00)`
11954	`- centuries (score: 1.00) → century`
11955	`- added (score: 1.00) → add`
11956	`Total keywords: 19 extracted in 0.0000 seconds`
11957
11958	`YAKE Keywords:`
11959	`- BROCK SLABACH (score: 0.00) → BROCK SLABACH`
11960	`- quality health care (score: 0.01)`
11961	`- higher poverty (score: 0.02) → high poverty`
11962	`- lower rates (score: 0.02) → low rate`
11963	`- health care (score: 0.02)`
11964	`- rates of education (score: 0.03) → rate of education`
11965	`- access to quality (score: 0.03)`
11966	`- quality health (score: 0.05)`
11967	`- BROCK (score: 0.06) → BROCK`
11968	`- SLABACH (score: 0.06) → SLABACH`
11969	`- care (score: 0.07)`
11970	`- health (score: 0.09)`
11971	`- poverty (score: 0.12)`
11972	`- lower (score: 0.12) → low`
11973	`- ball (score: 0.16)`
11974	`- higher (score: 0.16) → high`
11975	`- rates (score: 0.16) → rate`
11976	`- education (score: 0.16)`
11977	`- access (score: 0.16)`
11978	`- quality (score: 0.16)`
11979	`Total keywords: 20 extracted in 0.0097 seconds`
11980
11981	`KeyBERT Keywords:`
11982	`- health care pandemic (score: 0.47)`
11983	`- health health care (score: 0.46)`
11984	`- health care (score: 0.45)`
11985	`- quality health care (score: 0.45)`
11986	`- ball higher poverty (score: 0.45)`
11987	`- care pandemic (score: 0.43)`
11988	`- health health (score: 0.43)`
11989	`- information health health (score: 0.42)`
11990	`- information health (score: 0.41)`
11991	`- access quality health (score: 0.41)`
11992	`- health (score: 0.41)`
11993	`- care pandemic ve (score: 0.40)`
11994	`- quality health (score: 0.40)`
11995	`- pandemic (score: 0.39)`
11996	`- source information health (score: 0.37)`
11997	`- higher poverty (score: 0.37)`
11998	`- lower rates education (score: 0.35)`
11999	`- pandemic ve (score: 0.34)`
12000	`- higher poverty lower (score: 0.33)`
12001	`- education access quality (score: 0.31)`
12002	`Total keywords: 20 extracted in 0.0452 seconds`
12003
12004	`Dependency Relations (extracted in 0.0221sec):`
12005
12006	`Sentence 1: BROCK SLABACH: We're already behind the eight ball because we have higher poverty, lower rates of education and less access to quality health care.`
12007	`BROCK (PROPN) --[compound]--> SLABACH (PROPN)`
12008	`SLABACH (PROPN) --[npadvmod]--> 're (AUX)`
12009	`: (PUNCT) --[punct]--> SLABACH (PROPN)`
12010	`We (PRON) --[nsubj]--> 're (AUX)`
12011	`'re (AUX) --[ROOT]--> 're (AUX)`
12012	`already (ADV) --[advmod]--> 're (AUX)`
12013	`behind (ADP) --[prep]--> 're (AUX)`
12014	`the (DET) --[det]--> ball (NOUN)`
12015	`eight (NUM) --[nummod]--> ball (NOUN)`
12016	`ball (NOUN) --[pobj]--> behind (ADP)`
12017	`because (SCONJ) --[mark]--> have (VERB)`
12018	`we (PRON) --[nsubj]--> have (VERB)`
12019	`have (VERB) --[advcl]--> 're (AUX)`
12020	`higher (ADJ) --[amod]--> poverty (NOUN)`
12021	`poverty (NOUN) --[dobj]--> have (VERB)`
12022	`, (PUNCT) --[punct]--> poverty (NOUN)`
12023	`lower (ADJ) --[amod]--> rates (NOUN)`
12024	`rates (NOUN) --[conj]--> poverty (NOUN)`
12025	`of (ADP) --[prep]--> rates (NOUN)`
12026	`education (NOUN) --[pobj]--> of (ADP)`
12027	`and (CCONJ) --[cc]--> rates (NOUN)`
12028	`less (ADJ) --[amod]--> access (NOUN)`
12029	`access (NOUN) --[conj]--> rates (NOUN)`
12030	`to (ADP) --[prep]--> access (NOUN)`
12031	`quality (NOUN) --[compound]--> care (NOUN)`
12032	`health (NOUN) --[compound]--> care (NOUN)`
12033	`care (NOUN) --[pobj]--> to (ADP)`
12034	`. (PUNCT) --[punct]--> 're (AUX)`
12035
12036	`Sentence 2: And now through the pandemic, we've added this issue of mistrust in the very professionals that, for centuries, we've valued as an important source of information for health and health care.`
12037	`And (CCONJ) --[cc]--> added (VERB)`
12038	`now (ADV) --[advmod]--> added (VERB)`
12039	`through (ADP) --[prep]--> added (VERB)`
12040	`the (DET) --[det]--> pandemic (NOUN)`
12041	`pandemic (NOUN) --[pobj]--> through (ADP)`
12042	`, (PUNCT) --[punct]--> added (VERB)`
12043	`we (PRON) --[nsubj]--> added (VERB)`
12044	`'ve (AUX) --[aux]--> added (VERB)`
12045	`added (VERB) --[ROOT]--> added (VERB)`
12046	`this (DET) --[det]--> issue (NOUN)`
12047	`issue (NOUN) --[dobj]--> added (VERB)`
12048	`of (ADP) --[prep]--> issue (NOUN)`
12049	`mistrust (NOUN) --[pobj]--> of (ADP)`
12050	`in (ADP) --[prep]--> added (VERB)`
12051	`the (DET) --[det]--> professionals (NOUN)`
12052	`very (ADJ) --[amod]--> professionals (NOUN)`
12053	`professionals (NOUN) --[pobj]--> in (ADP)`
12054	`that (PRON) --[dobj]--> valued (VERB)`
12055	`, (PUNCT) --[punct]--> valued (VERB)`
12056	`for (ADP) --[prep]--> valued (VERB)`
12057	`centuries (NOUN) --[pobj]--> for (ADP)`
12058	`, (PUNCT) --[punct]--> valued (VERB)`
12059	`we (PRON) --[nsubj]--> valued (VERB)`
12060	`'ve (AUX) --[aux]--> valued (VERB)`
12061	`valued (VERB) --[relcl]--> professionals (NOUN)`
12062	`as (ADP) --[prep]--> valued (VERB)`
12063	`an (DET) --[det]--> source (NOUN)`
12064	`important (ADJ) --[amod]--> source (NOUN)`
12065	`source (NOUN) --[pobj]--> as (ADP)`
12066	`of (ADP) --[prep]--> source (NOUN)`
12067	`information (NOUN) --[pobj]--> of (ADP)`
12068	`for (ADP) --[prep]--> information (NOUN)`
12069	`health (NOUN) --[nmod]--> care (NOUN)`
12070	`and (CCONJ) --[cc]--> health (NOUN)`
12071	`health (NOUN) --[conj]--> health (NOUN)`
12072	`care (NOUN) --[pobj]--> for (ADP)`
12073	`. (PUNCT) --[punct]--> added (VERB)`
12074
12075	`Total sentences: 2`
12076
12077	`RAKE Keyphrase Relationships:`
12078	`noun phrase: "quality health care" contains [quality health care], [health care], [health]`
12079	`noun phrase: "health and health care" contains [health care], [health]`
12080	`verb phrase: "added now through 've issue in" contains [issue], [added]`
12081	`Total relationships found: 3`
12082
12083	`YAKE Keyphrase Relationships:`
12084	`noun phrase: "higher poverty" contains [higher poverty], [poverty], [higher]`
12085	`noun phrase: "lower rates" contains [lower rates], [lower], [rates]`
12086	`noun phrase: "quality health care" contains [quality health care], [health care], [quality health], [care], [health], [quality]`
12087	`noun phrase: "health and health care" contains [health care], [care], [health]`
12088	`Total relationships found: 4`
12089
12090	`Timing Comparison:`
12091	`RAKE: 0.0000sec`
12092	`YAKE: 0.0097sec`
12093	`KeyBERT: 0.0452sec`
12094	`Dependencies: 0.0221sec`
12095	`Fastest: RAKE`
12096
12097	`================================================================================`
12098	`Message 88:`
12099	`CHANG: Can I just say, that is a perfect example of how your book helped me look at the original "Pride And Prejudice" in a new way because in the original book, Mrs. Bennett always annoyed me with her hungry, openly grasping class ambitions.`
12100	`--------------------------------------------------------------------------------`
12101	`RAKE Keywords:`
12102	`- bennett always annoyed (score: 9.00) → Bennett always annoy`
12103	`- perfect example (score: 4.00)`
12104	`- new way (score: 4.00)`
12105	`- book helped (score: 4.00) → book help`
12106	`- original book (score: 3.50)`
12107	`- original (score: 1.50)`
12108	`- say (score: 1.00)`
12109	`- pride (score: 1.00) → Pride`
12110	`- prejudice (score: 1.00) → Prejudice`
12111	`- mrs (score: 1.00)`
12112	`- look (score: 1.00)`
12113	`- hungry (score: 1.00)`
12114	`- chang (score: 1.00) → CHANG`
12115	`Total keywords: 13 extracted in 0.0000 seconds`
12116
12117	`YAKE Keywords:`
12118	`- Pride And Prejudice (score: 0.00) → Pride and Prejudice`
12119	`- openly grasping class (score: 0.00) → openly grasp class`
12120	`- grasping class ambitions (score: 0.00) → grasp class ambition`
12121	`- Mrs. Bennett (score: 0.01) → Mrs. Bennett`
12122	`- Bennett always annoyed (score: 0.02) → Bennett always annoy`
12123	`- openly grasping (score: 0.02) → openly grasp`
12124	`- class ambitions (score: 0.02) → class ambition`
12125	`- CHANG (score: 0.03) → CHANG`
12126	`- grasping class (score: 0.03) → grasp class`
12127	`- book helped (score: 0.05) → book help`
12128	`- original book (score: 0.06)`
12129	`- Pride (score: 0.06) → Pride`
12130	`- Prejudice (score: 0.06) → Prejudice`
12131	`- Mrs. (score: 0.06) → Mrs.`
12132	`- Bennett (score: 0.09) → Bennett`
12133	`- original (score: 0.10)`
12134	`- hungry (score: 0.11)`
12135	`- openly (score: 0.11)`
12136	`- ambitions (score: 0.11) → ambition`
12137	`- book (score: 0.13)`
12138	`Total keywords: 20 extracted in 0.0190 seconds`
12139
12140	`KeyBERT Keywords:`
12141	`- original pride prejudice (score: 0.58)`
12142	`- pride prejudice new (score: 0.56)`
12143	`- pride prejudice (score: 0.56)`
12144	`- book mrs bennett (score: 0.51)`
12145	`- prejudice new (score: 0.51)`
12146	`- prejudice new way (score: 0.50)`
12147	`- class ambitions (score: 0.47)`
12148	`- prejudice (score: 0.47)`
12149	`- grasping class ambitions (score: 0.47)`
12150	`- mrs bennett annoyed (score: 0.46)`
12151	`- mrs bennett (score: 0.43)`
12152	`- original pride (score: 0.43)`
12153	`- original book mrs (score: 0.41)`
12154	`- perfect example book (score: 0.41)`
12155	`- look original pride (score: 0.41)`
12156	`- book helped look (score: 0.40)`
12157	`- example book helped (score: 0.40)`
12158	`- book mrs (score: 0.40)`
12159	`- ambitions (score: 0.39)`
12160	`- original book (score: 0.39)`
12161	`Total keywords: 20 extracted in 0.0415 seconds`
12162
12163	`Dependency Relations (extracted in 0.0077sec):`
12164
12165	`Sentence 1: CHANG: Can I just say, that is a perfect example of how your book helped me look at the original "Pride And Prejudice" in a new way because in the original book, Mrs. Bennett always annoyed me with her hungry, openly grasping class ambitions.`
12166	`CHANG (PROPN) --[dobj]--> say (VERB)`
12167	`: (PUNCT) --[punct]--> CHANG (PROPN)`
12168	`Can (AUX) --[aux]--> say (VERB)`
12169	`I (PRON) --[nsubj]--> say (VERB)`
12170	`just (ADV) --[advmod]--> say (VERB)`
12171	`say (VERB) --[ROOT]--> say (VERB)`
12172	`, (PUNCT) --[punct]--> say (VERB)`
12173	`that (PRON) --[nsubj]--> is (AUX)`
12174	`is (AUX) --[ccomp]--> say (VERB)`
12175	`a (DET) --[det]--> example (NOUN)`
12176	`perfect (ADJ) --[amod]--> example (NOUN)`
12177	`example (NOUN) --[attr]--> is (AUX)`
12178	`of (ADP) --[prep]--> example (NOUN)`
12179	`how (SCONJ) --[advmod]--> helped (VERB)`
12180	`your (PRON) --[poss]--> book (NOUN)`
12181	`book (NOUN) --[nsubj]--> helped (VERB)`
12182	`helped (VERB) --[pcomp]--> of (ADP)`
12183	`me (PRON) --[nsubj]--> look (VERB)`
12184	`look (VERB) --[ccomp]--> helped (VERB)`
12185	`at (ADP) --[prep]--> look (VERB)`
12186	`the (DET) --[det]--> Prejudice (PROPN)`
12187	`original (ADJ) --[amod]--> Prejudice (PROPN)`
12188	`" (PUNCT) --[punct]--> Prejudice (PROPN)`
12189	`Pride (PROPN) --[nmod]--> Prejudice (PROPN)`
12190	`And (CCONJ) --[cc]--> Pride (PROPN)`
12191	`Prejudice (PROPN) --[pobj]--> at (ADP)`
12192	`" (PUNCT) --[punct]--> Prejudice (PROPN)`
12193	`in (ADP) --[prep]--> look (VERB)`
12194	`a (DET) --[det]--> way (NOUN)`
12195	`new (ADJ) --[amod]--> way (NOUN)`
12196	`way (NOUN) --[pobj]--> in (ADP)`
12197	`because (SCONJ) --[mark]--> annoyed (VERB)`
12198	`in (ADP) --[prep]--> annoyed (VERB)`
12199	`the (DET) --[det]--> book (NOUN)`
12200	`original (ADJ) --[amod]--> book (NOUN)`
12201	`book (NOUN) --[pobj]--> in (ADP)`
12202	`, (PUNCT) --[punct]--> annoyed (VERB)`
12203	`Mrs. (PROPN) --[compound]--> Bennett (PROPN)`
12204	`Bennett (PROPN) --[nsubj]--> annoyed (VERB)`
12205	`always (ADV) --[advmod]--> annoyed (VERB)`
12206	`annoyed (VERB) --[advcl]--> helped (VERB)`
12207	`me (PRON) --[dobj]--> annoyed (VERB)`
12208	`with (ADP) --[prep]--> annoyed (VERB)`
12209	`her (PRON) --[poss]--> ambitions (NOUN)`
12210	`hungry (ADJ) --[amod]--> grasping (VERB)`
12211	`, (PUNCT) --[punct]--> grasping (VERB)`
12212	`openly (ADV) --[advmod]--> grasping (VERB)`
12213	`grasping (VERB) --[amod]--> ambitions (NOUN)`
12214	`class (NOUN) --[compound]--> ambitions (NOUN)`
12215	`ambitions (NOUN) --[pobj]--> with (ADP)`
12216	`. (PUNCT) --[punct]--> say (VERB)`
12217
12218	`Total sentences: 1`
12219
12220	`RAKE Keyphrase Relationships:`
12221	`noun phrase: "the original "Pride And Prejudice" contains [original], [pride], [prejudice]`
12222	`noun phrase: "the original book" contains [original book], [original]`
12223	`verb phrase: "say CHANG Can just" contains [say], [chang]`
12224	`Total relationships found: 3`
12225
12226	`YAKE Keyphrase Relationships:`
12227	`noun phrase: "the original "Pride And Prejudice" contains [pride and prejudice], [pride], [prejudice], [original]`
12228	`noun phrase: "the original book" contains [original book], [original], [book]`
12229	`noun phrase: "Mrs. Bennett" contains [mrs. bennett], [mrs.], [bennett]`
12230	`noun phrase: "her hungry, openly grasping class ambitions" contains [openly grasping class], [grasping class ambitions], [openly grasping], [class ambitions], [grasping class], [hungry], [openly], [ambitions]`
12231	`Total relationships found: 4`
12232
12233	`Timing Comparison:`
12234	`RAKE: 0.0000sec`
12235	`YAKE: 0.0190sec`
12236	`KeyBERT: 0.0415sec`
12237	`Dependencies: 0.0077sec`
12238	`Fastest: RAKE`
12239
12240	`================================================================================`
12241	`Message 89:`
12242	`HAMILTON: In other words, Manoli says...`
12243	`--------------------------------------------------------------------------------`
12244	`RAKE Keywords:`
12245	`- manoli says ... (score: 9.00)`
12246	`- words (score: 1.00) → word`
12247	`- hamilton (score: 1.00) → HAMILTON`
12248	`Total keywords: 3 extracted in 0.0000 seconds`
12249
12250	`YAKE Keywords:`
12251	`- HAMILTON (score: 0.03) → HAMILTON`
12252	`- Manoli (score: 0.09) → Manoli`
12253	`- words (score: 0.16) → word`
12254	`Total keywords: 3 extracted in 0.0000 seconds`
12255
12256	`KeyBERT Keywords:`
12257	`- hamilton words manoli (score: 0.86)`
12258	`- manoli says (score: 0.77)`
12259	`- words manoli says (score: 0.77)`
12260	`- manoli (score: 0.70)`
12261	`- words manoli (score: 0.70)`
12262	`- hamilton (score: 0.63)`
12263	`- hamilton words (score: 0.58)`
12264	`- says (score: 0.26)`
12265	`- words (score: 0.17)`
12266	`Total keywords: 9 extracted in 0.0240 seconds`
12267
12268	`Dependency Relations (extracted in 0.0060sec):`
12269
12270	`Sentence 1: HAMILTON:`
12271	`HAMILTON (PROPN) --[ROOT]--> HAMILTON (PROPN)`
12272	`: (PUNCT) --[punct]--> HAMILTON (PROPN)`
12273
12274	`Sentence 2: In other words, Manoli says...`
12275	`In (ADP) --[prep]--> says (VERB)`
12276	`other (ADJ) --[amod]--> words (NOUN)`
12277	`words (NOUN) --[pobj]--> In (ADP)`
12278	`, (PUNCT) --[punct]--> says (VERB)`
12279	`Manoli (PROPN) --[nsubj]--> says (VERB)`
12280	`says (VERB) --[ROOT]--> says (VERB)`
12281	`... (PUNCT) --[punct]--> says (VERB)`
12282
12283	`Total sentences: 2`
12284
12285	`RAKE Keyphrase Relationships:`
12286	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
12287
12288	`YAKE Keyphrase Relationships:`
12289	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
12290
12291	`Timing Comparison:`
12292	`RAKE: 0.0000sec`
12293	`YAKE: 0.0000sec`
12294	`KeyBERT: 0.0240sec`
12295	`Dependencies: 0.0060sec`
12296	`Fastest: RAKE`
12297
12298	`================================================================================`
12299	`Message 90:`
12300	OTIS: Latin American diplomats are saying that they're open to things like tougher sanctions. Pence wants them to, for example, restrict visas to Maduro officials, to confiscate state oil company assets in their countries, to close off their banking systems. But even before today's meetings, Latin American leaders - including the vice president of Brazil - were explicitly ruling out military intervention in Venezuela.You know, right now there's a lot of unity in many parts of the world in terms of backing Guaido and isolating Maduro. And so there's a danger that this solidarity could break apart if there's a major push, either from within the Venezuelan opposition or the U.S. - or from some other country - to send in troops.
12301	`--------------------------------------------------------------------------------`
12302	`RAKE Keywords:`
12303	`- latin american leaders (score: 9.00) → latin american leader`
12304	`- latin american diplomats (score: 9.00) → latin american diplomat`
12305	`- vice president (score: 4.00)`
12306	`- venezuelan opposition (score: 4.00)`
12307	`- restrict visas (score: 4.00) → restrict visa`
12308	`- pence wants (score: 4.00) → Pence want`
12309	`- military intervention (score: 4.00)`
12310	`- many parts (score: 4.00) → many part`
12311	`- major push (score: 4.00)`
12312	`- maduro officials (score: 4.00) → Maduro official`
12313	`- isolating maduro (score: 4.00) → isolate Maduro`
12314	`- explicitly ruling (score: 4.00) → explicitly rule`
12315	`- banking systems (score: 4.00) → banking system`
12316	`- backing guaido (score: 4.00) → back Guaido`
12317	`- world (score: 1.00)`
12318	`- within (score: 1.00)`
12319	`- venezuela (score: 1.00) → Venezuela`
12320	`- unity (score: 1.00)`
12321	`- u (score: 1.00)`
12322	`- troops (score: 1.00) → troop`
12323	`- today (score: 1.00)`
12324	`- terms (score: 1.00) → term`
12325	`- send (score: 1.00)`
12326	`- saying (score: 1.00) → say`
12327	`- right (score: 1.00)`
12328	`- otis (score: 1.00) → OTIS`
12329	`- open (score: 1.00)`
12330	`- meetings (score: 1.00) → meeting`
12331	`- lot (score: 1.00)`
12332	`- know (score: 1.00)`
12333	`- including (score: 1.00) → include`
12334	`- example (score: 1.00)`
12335	`- even (score: 1.00)`
12336	`- either (score: 1.00)`
12337	`- danger (score: 1.00)`
12338	`- country (score: 1.00)`
12339	`- countries (score: 1.00) → country`
12340	`- close (score: 1.00)`
12341	`- brazil (score: 1.00) → Brazil`
12342	`Total keywords: 39 extracted in 0.0017 seconds`
12343
12344	`YAKE Keywords:`
12345	`- Latin American diplomats (score: 0.00) → latin american diplomat`
12346	`- Latin American leaders (score: 0.01) → latin american leader`
12347	`- Latin American (score: 0.01)`
12348	`- tougher sanctions (score: 0.02) → tough sanction`
12349	`- open to things (score: 0.02) → open to thing`
12350	`- things like tougher (score: 0.02) → thing like tough`
12351	`- American diplomats (score: 0.02) → american diplomat`
12352	`- OTIS (score: 0.04) → OTIS`
12353	`- Latin (score: 0.06)`
12354	`- American leaders (score: 0.07) → american leader`
12355	`- confiscate state oil (score: 0.07)`
12356	`- state oil company (score: 0.07)`
12357	`- oil company assets (score: 0.07) → oil company asset`
12358	`- Maduro officials (score: 0.07) → Maduro official`
12359	`- American (score: 0.08)`
12360	`- Maduro (score: 0.11) → Maduro`
12361	`- isolating Maduro (score: 0.11) → isolate Maduro`
12362	`- sanctions (score: 0.11) → sanction`
12363	`- president of Brazil (score: 0.12) → president of Brazil`
12364	`- restrict visas (score: 0.12) → restrict visa`
12365	`Total keywords: 20 extracted in 0.0267 seconds`
12366
12367	`KeyBERT Keywords:`
12368	`- military intervention venezuela (score: 0.63)`
12369	`- push venezuelan (score: 0.63)`
12370	`- push venezuelan opposition (score: 0.62)`
12371	`- major push venezuelan (score: 0.59)`
12372	`- intervention venezuela (score: 0.59)`
12373	`- intervention venezuela know (score: 0.56)`
12374	`- latin american diplomats (score: 0.55)`
12375	`- latin american leaders (score: 0.54)`
12376	`- venezuelan opposition (score: 0.53)`
12377	`- venezuelan opposition country (score: 0.51)`
12378	`- sanctions pence wants (score: 0.49)`
12379	`- venezuelan (score: 0.49)`
12380	`- sanctions pence (score: 0.48)`
12381	`- tougher sanctions pence (score: 0.47)`
12382	`- visas maduro officials (score: 0.45)`
12383	`- venezuela (score: 0.44)`
12384	`- meetings latin american (score: 0.43)`
12385	`- venezuela know (score: 0.42)`
12386	`- guaido isolating maduro (score: 0.41)`
12387	`- venezuela know right (score: 0.41)`
12388	`Total keywords: 20 extracted in 0.1047 seconds`
12389
12390	`Dependency Relations (extracted in 0.0212sec):`
12391
12392	`Sentence 1: OTIS:`
12393	`OTIS (PROPN) --[ROOT]--> OTIS (PROPN)`
12394	`: (PUNCT) --[punct]--> OTIS (PROPN)`
12395
12396	`Sentence 2: Latin American diplomats are saying that they're open to things like tougher sanctions.`
12397	`Latin (ADJ) --[amod]--> American (ADJ)`
12398	`American (ADJ) --[amod]--> diplomats (NOUN)`
12399	`diplomats (NOUN) --[nsubj]--> saying (VERB)`
12400	`are (AUX) --[aux]--> saying (VERB)`
12401	`saying (VERB) --[ROOT]--> saying (VERB)`
12402	`that (SCONJ) --[mark]--> 're (AUX)`
12403	`they (PRON) --[nsubj]--> 're (AUX)`
12404	`'re (AUX) --[ccomp]--> saying (VERB)`
12405	`open (ADJ) --[acomp]--> 're (AUX)`
12406	`to (ADP) --[prep]--> open (ADJ)`
12407	`things (NOUN) --[pobj]--> to (ADP)`
12408	`like (ADP) --[prep]--> things (NOUN)`
12409	`tougher (ADJ) --[amod]--> sanctions (NOUN)`
12410	`sanctions (NOUN) --[pobj]--> like (ADP)`
12411	`. (PUNCT) --[punct]--> saying (VERB)`
12412
12413	`Sentence 3: Pence wants them to, for example, restrict visas to Maduro officials, to confiscate state oil company assets in their countries, to close off their banking systems.`
12414	`Pence (PROPN) --[nsubj]--> wants (VERB)`
12415	`wants (VERB) --[ROOT]--> wants (VERB)`
12416	`them (PRON) --[nsubj]--> restrict (VERB)`
12417	`to (PART) --[aux]--> restrict (VERB)`
12418	`, (PUNCT) --[punct]--> restrict (VERB)`
12419	`for (ADP) --[prep]--> restrict (VERB)`
12420	`example (NOUN) --[pobj]--> for (ADP)`
12421	`, (PUNCT) --[punct]--> restrict (VERB)`
12422	`restrict (VERB) --[ccomp]--> wants (VERB)`
12423	`visas (NOUN) --[dobj]--> restrict (VERB)`
12424	`to (ADP) --[prep]--> restrict (VERB)`
12425	`Maduro (PROPN) --[compound]--> officials (NOUN)`
12426	`officials (NOUN) --[pobj]--> to (ADP)`
12427	`, (PUNCT) --[punct]--> restrict (VERB)`
12428	`to (PART) --[aux]--> confiscate (VERB)`
12429	`confiscate (VERB) --[advcl]--> restrict (VERB)`
12430	`state (NOUN) --[compound]--> assets (NOUN)`
12431	`oil (NOUN) --[compound]--> company (NOUN)`
12432	`company (NOUN) --[compound]--> assets (NOUN)`
12433	`assets (NOUN) --[dobj]--> confiscate (VERB)`
12434	`in (ADP) --[prep]--> confiscate (VERB)`
12435	`their (PRON) --[poss]--> countries (NOUN)`
12436	`countries (NOUN) --[pobj]--> in (ADP)`
12437	`, (PUNCT) --[punct]--> confiscate (VERB)`
12438	`to (PART) --[aux]--> close (VERB)`
12439	`close (VERB) --[advcl]--> confiscate (VERB)`
12440	`off (ADP) --[prt]--> close (VERB)`
12441	`their (PRON) --[poss]--> systems (NOUN)`
12442	`banking (NOUN) --[compound]--> systems (NOUN)`
12443	`systems (NOUN) --[dobj]--> close (VERB)`
12444	`. (PUNCT) --[punct]--> wants (VERB)`
12445
12446	`Sentence 4: But even before today's meetings, Latin American leaders - including the vice president of Brazil - were explicitly ruling out military intervention in Venezuela.`
12447	`But (CCONJ) --[cc]--> ruling (VERB)`
12448	`even (ADV) --[advmod]--> before (ADP)`
12449	`before (ADP) --[prep]--> ruling (VERB)`
12450	`today (NOUN) --[poss]--> meetings (NOUN)`
12451	`'s (PART) --[case]--> today (NOUN)`
12452	`meetings (NOUN) --[pobj]--> before (ADP)`
12453	`, (PUNCT) --[punct]--> meetings (NOUN)`
12454	`Latin (ADJ) --[amod]--> American (ADJ)`
12455	`American (ADJ) --[amod]--> leaders (NOUN)`
12456	`leaders (NOUN) --[appos]--> meetings (NOUN)`
12457	`- (PUNCT) --[punct]--> leaders (NOUN)`
12458	`including (VERB) --[prep]--> leaders (NOUN)`
12459	`the (DET) --[det]--> president (NOUN)`
12460	`vice (NOUN) --[compound]--> president (NOUN)`
12461	`president (NOUN) --[pobj]--> including (VERB)`
12462	`of (ADP) --[prep]--> president (NOUN)`
12463	`Brazil (PROPN) --[pobj]--> of (ADP)`
12464	`- (PUNCT) --[punct]--> meetings (NOUN)`
12465	`were (AUX) --[aux]--> ruling (VERB)`
12466	`explicitly (ADV) --[advmod]--> ruling (VERB)`
12467	`ruling (VERB) --[ROOT]--> ruling (VERB)`
12468	`out (ADP) --[prt]--> ruling (VERB)`
12469	`military (ADJ) --[amod]--> intervention (NOUN)`
12470	`intervention (NOUN) --[dobj]--> ruling (VERB)`
12471	`in (ADP) --[prep]--> intervention (NOUN)`
12472	`Venezuela (PROPN) --[pobj]--> in (ADP)`
12473	`. (PUNCT) --[punct]--> ruling (VERB)`
12474
12475	`Sentence 5: You know, right now there's a lot of unity in many parts of the world in terms of backing Guaido and isolating Maduro.`
12476	`You (PRON) --[nsubj]--> know (VERB)`
12477	`know (VERB) --[parataxis]--> 's (VERB)`
12478	`, (PUNCT) --[punct]--> 's (VERB)`
12479	`right (ADV) --[advmod]--> now (ADV)`
12480	`now (ADV) --[advmod]--> 's (VERB)`
12481	`there (PRON) --[expl]--> 's (VERB)`
12482	`'s (VERB) --[ROOT]--> 's (VERB)`
12483	`a (DET) --[det]--> lot (NOUN)`
12484	`lot (NOUN) --[attr]--> 's (VERB)`
12485	`of (ADP) --[prep]--> lot (NOUN)`
12486	`unity (NOUN) --[pobj]--> of (ADP)`
12487	`in (ADP) --[prep]--> lot (NOUN)`
12488	`many (ADJ) --[amod]--> parts (NOUN)`
12489	`parts (NOUN) --[pobj]--> in (ADP)`
12490	`of (ADP) --[prep]--> parts (NOUN)`
12491	`the (DET) --[det]--> world (NOUN)`
12492	`world (NOUN) --[pobj]--> of (ADP)`
12493	`in (ADP) --[prep]--> 's (VERB)`
12494	`terms (NOUN) --[pobj]--> in (ADP)`
12495	`of (ADP) --[prep]--> terms (NOUN)`
12496	`backing (VERB) --[pcomp]--> of (ADP)`
12497	`Guaido (PROPN) --[dobj]--> backing (VERB)`
12498	`and (CCONJ) --[cc]--> backing (VERB)`
12499	`isolating (VERB) --[conj]--> backing (VERB)`
12500	`Maduro (PROPN) --[dobj]--> isolating (VERB)`
12501	`. (PUNCT) --[punct]--> 's (VERB)`
12502
12503	`Sentence 6: And so there's a danger that this solidarity could break apart if there's a major push, either from within the Venezuelan opposition or the U.S. - or from some other country - to send in troops.`
12504	`And (CCONJ) --[cc]--> 's (VERB)`
12505	`so (ADV) --[advmod]--> 's (VERB)`
12506	`there (PRON) --[expl]--> 's (VERB)`
12507	`'s (VERB) --[ROOT]--> 's (VERB)`
12508	`a (DET) --[det]--> danger (NOUN)`
12509	`danger (NOUN) --[attr]--> 's (VERB)`
12510	`that (PRON) --[dobj]--> break (VERB)`
12511	`this (DET) --[det]--> solidarity (NOUN)`
12512	`solidarity (NOUN) --[nsubj]--> break (VERB)`
12513	`could (AUX) --[aux]--> break (VERB)`
12514	`break (VERB) --[relcl]--> danger (NOUN)`
12515	`apart (ADV) --[advmod]--> break (VERB)`
12516	`if (SCONJ) --[mark]--> 's (VERB)`
12517	`there (PRON) --[expl]--> 's (VERB)`
12518	`'s (VERB) --[advcl]--> break (VERB)`
12519	`a (DET) --[det]--> push (NOUN)`
12520	`major (ADJ) --[amod]--> push (NOUN)`
12521	`push (NOUN) --[attr]--> 's (VERB)`
12522	`, (PUNCT) --[punct]--> break (VERB)`
12523	`either (CCONJ) --[preconj]--> from (ADP)`
12524	`from (ADP) --[prep]--> send (VERB)`
12525	`within (ADP) --[prep]--> from (ADP)`
12526	`the (DET) --[det]--> opposition (NOUN)`
12527	`Venezuelan (ADJ) --[amod]--> opposition (NOUN)`
12528	`opposition (NOUN) --[pobj]--> within (ADP)`
12529	`or (CCONJ) --[cc]--> opposition (NOUN)`
12530	`the (DET) --[det]--> U.S. (PROPN)`
12531	`U.S. (PROPN) --[conj]--> opposition (NOUN)`
12532	`- (PUNCT) --[punct]--> within (ADP)`
12533	`or (CCONJ) --[cc]--> within (ADP)`
12534	`from (ADP) --[conj]--> within (ADP)`
12535	`some (DET) --[det]--> country (NOUN)`
12536	`other (ADJ) --[amod]--> country (NOUN)`
12537	`country (NOUN) --[pobj]--> from (ADP)`
12538	`- (PUNCT) --[punct]--> from (ADP)`
12539	`to (PART) --[aux]--> send (VERB)`
12540	`send (VERB) --[advcl]--> break (VERB)`
12541	`in (ADP) --[prt]--> send (VERB)`
12542	`troops (NOUN) --[dobj]--> send (VERB)`
12543	`. (PUNCT) --[punct]--> 's (VERB)`
12544
12545	`Total sentences: 6`
12546
12547	`RAKE Keyphrase Relationships:`
12548	`noun phrase: "Maduro officials" contains [maduro officials], [u]`
12549	`noun phrase: "their countries" contains [u], [countries]`
12550	`noun phrase: "today's meetings" contains [today], [meetings]`
12551	`noun phrase: "Venezuela" contains [venezuela], [u]`
12552	`noun phrase: "unity" contains [unity], [u]`
12553	`noun phrase: "a major push" contains [major push], [u]`
12554	`noun phrase: "the Venezuelan opposition" contains [venezuelan opposition], [venezuela], [u]`
12555	`noun phrase: "some other country" contains [u], [country]`
12556	`verb phrase: "including president" contains [u], [including]`
12557	`verb phrase: "backing Guaido" contains [backing guaido], [u]`
12558	`verb phrase: "isolating Maduro" contains [isolating maduro], [u]`
12559	`verb phrase: "send from to troops" contains [troops], [send]`
12560	`Total relationships found: 12`
12561
12562	`YAKE Keyphrase Relationships:`
12563	`noun phrase: "Latin American diplomats" contains [latin american diplomats], [latin american], [american diplomats], [latin], [american]`
12564	`noun phrase: "tougher sanctions" contains [tougher sanctions], [sanctions]`
12565	`noun phrase: "Maduro officials" contains [maduro officials], [maduro]`
12566	`noun phrase: "state oil company assets" contains [state oil company], [oil company assets]`
12567	`noun phrase: "Latin American leaders" contains [latin american leaders], [latin american], [latin], [american leaders], [american]`
12568	`verb phrase: "isolating Maduro" contains [latin], [maduro], [isolating maduro]`
12569	`Total relationships found: 6`
12570
12571	`Timing Comparison:`
12572	`RAKE: 0.0017sec`
12573	`YAKE: 0.0267sec`
12574	`KeyBERT: 0.1047sec`
12575	`Dependencies: 0.0212sec`
12576	`Fastest: RAKE`
12577
12578	`================================================================================`
12579	`Message 91:`
12580	`FOLKENFLIK: You bet.(SOUNDBITE OF ALLAH-LAS' "RASPBERRY JAM")`
12581	`--------------------------------------------------------------------------------`
12582	`RAKE Keywords:`
12583	`- raspberry jam ") (score: 9.00)`
12584	`- soundbite (score: 1.00)`
12585	`- las (score: 1.00) → LAS`
12586	`- folkenflik (score: 1.00)`
12587	`- bet (score: 1.00)`
12588	`- allah (score: 1.00) → ALLAH`
12589	`Total keywords: 6 extracted in 0.0000 seconds`
12590
12591	`YAKE Keywords:`
12592	`- SOUNDBITE OF ALLAH-LAS (score: 0.01)`
12593	`- RASPBERRY JAM (score: 0.01) → RASPBERRY JAM`
12594	`- FOLKENFLIK (score: 0.03)`
12595	`- SOUNDBITE (score: 0.09)`
12596	`- ALLAH-LAS (score: 0.09)`
12597	`- RASPBERRY (score: 0.09) → RASPBERRY`
12598	`- JAM (score: 0.09) → JAM`
12599	`- bet. (score: 0.16)`
12600	`Total keywords: 8 extracted in 0.0000 seconds`
12601
12602	`KeyBERT Keywords:`
12603	`- folkenflik bet soundbite (score: 0.65)`
12604	`- allah las raspberry (score: 0.64)`
12605	`- bet soundbite allah (score: 0.59)`
12606	`- folkenflik bet (score: 0.59)`
12607	`- folkenflik (score: 0.59)`
12608	`- raspberry jam (score: 0.57)`
12609	`- las raspberry jam (score: 0.55)`
12610	`- soundbite allah (score: 0.55)`
12611	`- soundbite allah las (score: 0.52)`
12612	`- jam (score: 0.45)`
12613	`- bet soundbite (score: 0.43)`
12614	`- allah (score: 0.40)`
12615	`- las raspberry (score: 0.40)`
12616	`- allah las (score: 0.39)`
12617	`- raspberry (score: 0.39)`
12618	`- soundbite (score: 0.36)`
12619	`- bet (score: 0.24)`
12620	`- las (score: 0.09)`
12621	`Total keywords: 18 extracted in 0.0297 seconds`
12622
12623	`Dependency Relations (extracted in 0.0000sec):`
12624
12625	`Sentence 1: FOLKENFLIK:`
12626	`FOLKENFLIK (NOUN) --[ROOT]--> FOLKENFLIK (NOUN)`
12627	`: (PUNCT) --[punct]--> FOLKENFLIK (NOUN)`
12628
12629	`Sentence 2: You bet.(SOUNDBITE OF ALLAH-LAS' "RASPBERRY JAM")`
12630	`You (PRON) --[ROOT]--> You (PRON)`
12631	`bet.(SOUNDBITE (NOUN) --[appos]--> You (PRON)`
12632	`OF (ADP) --[prep]--> bet.(SOUNDBITE (NOUN)`
12633	`ALLAH (PROPN) --[compound]--> LAS (PROPN)`
12634	`- (PUNCT) --[punct]--> LAS (PROPN)`
12635	`LAS (PROPN) --[pobj]--> OF (ADP)`
12636	`' (PUNCT) --[punct]--> LAS (PROPN)`
12637	`" (PUNCT) --[punct]--> JAM (PROPN)`
12638	`RASPBERRY (PROPN) --[compound]--> JAM (PROPN)`
12639	`JAM (PROPN) --[pobj]--> OF (ADP)`
12640	`" (PUNCT) --[punct]--> JAM (PROPN)`
12641	`) (PUNCT) --[punct]--> You (PRON)`
12642
12643	`Total sentences: 2`
12644
12645	`RAKE Keyphrase Relationships:`
12646	`noun phrase: "bet.(SOUNDBITE" contains [soundbite], [bet]`
12647	`noun phrase: "ALLAH-LAS" contains [las], [allah]`
12648	`Total relationships found: 2`
12649
12650	`YAKE Keyphrase Relationships:`
12651	`noun phrase: "bet.(SOUNDBITE" contains [soundbite], [bet.]`
12652	`noun phrase: ""RASPBERRY JAM" contains [raspberry jam], [raspberry], [jam]`
12653	`Total relationships found: 2`
12654
12655	`Timing Comparison:`
12656	`RAKE: 0.0000sec`
12657	`YAKE: 0.0000sec`
12658	`KeyBERT: 0.0297sec`
12659	`Dependencies: 0.0000sec`
12660	`Fastest: RAKE`
12661
12662	`================================================================================`
12663	`Message 92:`
12664	`SHAPIRO: What has the reaction today been from Puerto Rico's governor?`
12665	`--------------------------------------------------------------------------------`
12666	`RAKE Keywords:`
12667	`- reaction today (score: 4.00)`
12668	`- puerto rico (score: 4.00) → Puerto Rico`
12669	`- shapiro (score: 1.00) → SHAPIRO`
12670	`- governor (score: 1.00)`
12671	`Total keywords: 4 extracted in 0.0000 seconds`
12672
12673	`YAKE Keywords:`
12674	`- Puerto Rico governor (score: 0.00)`
12675	`- Puerto Rico (score: 0.02) → Puerto Rico`
12676	`- Rico governor (score: 0.02)`
12677	`- SHAPIRO (score: 0.03) → SHAPIRO`
12678	`- reaction today (score: 0.10)`
12679	`- Puerto (score: 0.14) → Puerto`
12680	`- Rico (score: 0.14) → Rico`
12681	`- governor (score: 0.16)`
12682	`- reaction (score: 0.30)`
12683	`- today (score: 0.30)`
12684	`Total keywords: 10 extracted in 0.0000 seconds`
12685
12686	`KeyBERT Keywords:`
12687	`- puerto rico governor (score: 0.72)`
12688	`- reaction today puerto (score: 0.71)`
12689	`- rico governor (score: 0.68)`
12690	`- today puerto rico (score: 0.59)`
12691	`- today puerto (score: 0.56)`
12692	`- puerto (score: 0.53)`
12693	`- rico (score: 0.52)`
12694	`- puerto rico (score: 0.52)`
12695	`- shapiro reaction today (score: 0.48)`
12696	`- governor (score: 0.42)`
12697	`- shapiro reaction (score: 0.40)`
12698	`- shapiro (score: 0.36)`
12699	`- reaction today (score: 0.34)`
12700	`- reaction (score: 0.23)`
12701	`- today (score: 0.17)`
12702	`Total keywords: 15 extracted in 0.0312 seconds`
12703
12704	`Dependency Relations (extracted in 0.0060sec):`
12705
12706	`Sentence 1: SHAPIRO: What has the reaction today been from Puerto Rico's governor?`
12707	`SHAPIRO (PROPN) --[ROOT]--> SHAPIRO (PROPN)`
12708	`: (PUNCT) --[punct]--> SHAPIRO (PROPN)`
12709	`What (PRON) --[nsubj]--> has (AUX)`
12710	`has (AUX) --[aux]--> been (AUX)`
12711	`the (DET) --[det]--> reaction (NOUN)`
12712	`reaction (NOUN) --[nsubj]--> been (AUX)`
12713	`today (NOUN) --[npadvmod]--> reaction (NOUN)`
12714	`been (AUX) --[acl]--> SHAPIRO (PROPN)`
12715	`from (ADP) --[prep]--> been (AUX)`
12716	`Puerto (PROPN) --[compound]--> Rico (PROPN)`
12717	`Rico (PROPN) --[poss]--> governor (NOUN)`
12718	`'s (PART) --[case]--> Rico (PROPN)`
12719	`governor (NOUN) --[pobj]--> from (ADP)`
12720	`? (PUNCT) --[punct]--> been (AUX)`
12721
12722	`Total sentences: 1`
12723
12724	`RAKE Keyphrase Relationships:`
12725	`noun phrase: "Puerto Rico's governor" contains [puerto rico], [governor]`
12726	`Total relationships found: 1`
12727
12728	`YAKE Keyphrase Relationships:`
12729	`noun phrase: "Puerto Rico's governor" contains [puerto rico], [puerto], [rico], [governor]`
12730	`Total relationships found: 1`
12731
12732	`Timing Comparison:`
12733	`RAKE: 0.0000sec`
12734	`YAKE: 0.0000sec`
12735	`KeyBERT: 0.0312sec`
12736	`Dependencies: 0.0060sec`
12737	`Fastest: RAKE`
12738
12739	`================================================================================`
12740	`Message 93:`
12741	MCLAUGHLIN: Right. It appears that they've been targeting both campaigns. It doesn't appear that they got into Biden or Harris' campaigns. They had more success, though, with Trump campaign emails. They've allegedly been sharing some of what they stole with journalists, including research on Trump's running mate, JD Vance, and also, most recently, the September letter from Trump's lawyer to The New York Times.Actually, today is the first time we've seen a journalist publish the Vance dossier in full. It was on an independent Substack, and the argument was that it sheds light on the Trump campaign's perceptions of Vance's weaknesses, and, you know, the public deserves to know. Though, the blogger, Ken Klippenstein, conceded that the information in the document is pretty much public already.
12742	`--------------------------------------------------------------------------------`
12743	`RAKE Keywords:`
12744	`- new york times (score: 9.00) → New York Times`
12745	`- trump campaign emails (score: 7.25) → Trump campaign email`
12746	`- trump campaign (score: 4.25) → Trump campaign`
12747	`- sheds light (score: 4.00) → shed light`
12748	`- september letter (score: 4.00) → September letter`
12749	`- running mate (score: 4.00)`
12750	`- public deserves (score: 4.00) → public deserve`
12751	`- ken klippenstein (score: 4.00) → Ken Klippenstein`
12752	`- journalist publish (score: 4.00)`
12753	`- independent substack (score: 4.00) → independent Substack`
12754	`- including research (score: 4.00) → include research`
12755	`- first time (score: 4.00)`
12756	`- vance dossier (score: 3.67) → Vance dossier`
12757	`- jd vance (score: 3.67) → JD Vance`
12758	`- trump (score: 1.75) → Trump`
12759	`- trump (score: 1.75) → Trump`
12760	`- vance (score: 1.67) → Vance`
12761	`- weaknesses (score: 1.00) → weakness`
12762	`- today (score: 1.00)`
12763	`- though (score: 1.00)`
12764	`- though (score: 1.00)`
12765	`- targeting (score: 1.00) → target`
12766	`- success (score: 1.00)`
12767	`- stole (score: 1.00) → steal`
12768	`- sharing (score: 1.00) → share`
12769	`- seen (score: 1.00) → see`
12770	`- right (score: 1.00)`
12771	`- recently (score: 1.00)`
12772	`- perceptions (score: 1.00) → perception`
12773	`- mclaughlin (score: 1.00) → MCLAUGHLIN`
12774	`- lawyer (score: 1.00)`
12775	`- know (score: 1.00)`
12776	`- know (score: 1.00)`
12777	`- journalists (score: 1.00) → journalist`
12778	`- information (score: 1.00)`
12779	`- harris (score: 1.00) → Harris`
12780	`- got (score: 1.00) → get`
12781	`- full (score: 1.00)`
12782	`- document (score: 1.00)`
12783	`- conceded (score: 1.00) → concede`
12784	`- campaigns (score: 1.00) → campaign`
12785	`- campaigns (score: 1.00) → campaign`
12786	`- blogger (score: 1.00)`
12787	`- biden (score: 1.00) → Biden`
12788	`- argument (score: 1.00)`
12789	`- appears (score: 1.00) → appear`
12790	`- appear (score: 1.00)`
12791	`- also (score: 1.00)`
12792	`- allegedly (score: 1.00)`
12793	`- actually (score: 1.00)`
12794	`Total keywords: 50 extracted in 0.0000 seconds`
12795
12796	`YAKE Keywords:`
12797	`- MCLAUGHLIN (score: 0.05) → MCLAUGHLIN`
12798	`- Trump campaign (score: 0.05) → Trump campaign`
12799	`- Trump (score: 0.07) → Trump`
12800	`- Trump campaign emails (score: 0.07) → Trump campaign email`
12801	`- Biden or Harris' (score: 0.08)`
12802	`- Trump campaign perceptions (score: 0.09)`
12803	`- Vance (score: 0.10) → Vance`
12804	`- Harris' campaigns (score: 0.11)`
12805	`- Ken Klippenstein (score: 0.14) → Ken Klippenstein`
12806	`- Trump running mate (score: 0.16)`
12807	`- campaigns (score: 0.18) → campaign`
12808	`- campaign (score: 0.18)`
12809	`- Trump running (score: 0.19)`
12810	`- Trump lawyer (score: 0.19)`
12811	`- Vance weaknesses (score: 0.21)`
12812	`- Vance dossier (score: 0.22) → Vance dossier`
12813	`- York Times.Actually (score: 0.23)`
12814	`- campaign emails (score: 0.23) → campaign email`
12815	`- September letter (score: 0.25) → September letter`
12816	`- independent Substack (score: 0.26) → independent Substack`
12817	`Total keywords: 20 extracted in 0.0350 seconds`
12818
12819	`KeyBERT Keywords:`
12820	`- publish vance dossier (score: 0.66)`
12821	`- vance dossier (score: 0.62)`
12822	`- vance dossier independent (score: 0.61)`
12823	`- journalist publish vance (score: 0.60)`
12824	`- campaign perceptions vance (score: 0.58)`
12825	`- biden harris campaigns (score: 0.56)`
12826	`- jd vance recently (score: 0.51)`
12827	`- publish vance (score: 0.50)`
12828	`- vance recently (score: 0.50)`
12829	`- harris campaigns success (score: 0.49)`
12830	`- harris campaigns (score: 0.48)`
12831	`- vance weaknesses (score: 0.47)`
12832	`- vance weaknesses know (score: 0.46)`
12833	`- jd vance (score: 0.46)`
12834	`- vance recently september (score: 0.45)`
12835	`- vance (score: 0.45)`
12836	`- mate jd vance (score: 0.43)`
12837	`- trump campaign emails (score: 0.43)`
12838	`- biden harris (score: 0.42)`
12839	`- perceptions vance (score: 0.41)`
12840	`Total keywords: 20 extracted in 0.1058 seconds`
12841
12842	`Dependency Relations (extracted in 0.0257sec):`
12843
12844	`Sentence 1: MCLAUGHLIN:`
12845	`MCLAUGHLIN (PROPN) --[ROOT]--> MCLAUGHLIN (PROPN)`
12846	`: (PUNCT) --[punct]--> MCLAUGHLIN (PROPN)`
12847
12848	`Sentence 2: Right.`
12849	`Right (INTJ) --[ROOT]--> Right (INTJ)`
12850	`. (PUNCT) --[punct]--> Right (INTJ)`
12851
12852	`Sentence 3: It appears that they've been targeting both campaigns.`
12853	`It (PRON) --[nsubj]--> appears (VERB)`
12854	`appears (VERB) --[ROOT]--> appears (VERB)`
12855	`that (SCONJ) --[mark]--> targeting (VERB)`
12856	`they (PRON) --[nsubj]--> targeting (VERB)`
12857	`'ve (AUX) --[aux]--> targeting (VERB)`
12858	`been (AUX) --[aux]--> targeting (VERB)`
12859	`targeting (VERB) --[ccomp]--> appears (VERB)`
12860	`both (DET) --[det]--> campaigns (NOUN)`
12861	`campaigns (NOUN) --[dobj]--> targeting (VERB)`
12862	`. (PUNCT) --[punct]--> appears (VERB)`
12863
12864	`Sentence 4: It doesn't appear that they got into Biden or Harris' campaigns.`
12865	`It (PRON) --[nsubj]--> appear (VERB)`
12866	`does (AUX) --[aux]--> appear (VERB)`
12867	`n't (PART) --[neg]--> appear (VERB)`
12868	`appear (VERB) --[ROOT]--> appear (VERB)`
12869	`that (SCONJ) --[mark]--> got (VERB)`
12870	`they (PRON) --[nsubj]--> got (VERB)`
12871	`got (VERB) --[ccomp]--> appear (VERB)`
12872	`into (ADP) --[prep]--> got (VERB)`
12873	`Biden (PROPN) --[poss]--> campaigns (NOUN)`
12874	`or (CCONJ) --[cc]--> Biden (PROPN)`
12875	`Harris (PROPN) --[conj]--> Biden (PROPN)`
12876	`' (PART) --[case]--> Harris (PROPN)`
12877	`campaigns (NOUN) --[pobj]--> into (ADP)`
12878	`. (PUNCT) --[punct]--> appear (VERB)`
12879
12880	`Sentence 5: They had more success, though, with Trump campaign emails.`
12881	`They (PRON) --[nsubj]--> had (VERB)`
12882	`had (VERB) --[ROOT]--> had (VERB)`
12883	`more (ADJ) --[amod]--> success (NOUN)`
12884	`success (NOUN) --[dobj]--> had (VERB)`
12885	`, (PUNCT) --[punct]--> had (VERB)`
12886	`though (ADV) --[advmod]--> had (VERB)`
12887	`, (PUNCT) --[punct]--> had (VERB)`
12888	`with (ADP) --[prep]--> had (VERB)`
12889	`Trump (PROPN) --[compound]--> emails (NOUN)`
12890	`campaign (NOUN) --[compound]--> emails (NOUN)`
12891	`emails (NOUN) --[pobj]--> with (ADP)`
12892	`. (PUNCT) --[punct]--> had (VERB)`
12893
12894	`Sentence 6: They've allegedly been sharing some of what they stole with journalists, including research on Trump's running mate, JD Vance, and also, most recently, the September letter from Trump's lawyer to The New York Times.`
12895	`They (PRON) --[nsubj]--> sharing (VERB)`
12896	`'ve (AUX) --[aux]--> sharing (VERB)`
12897	`allegedly (ADV) --[advmod]--> sharing (VERB)`
12898	`been (AUX) --[aux]--> sharing (VERB)`
12899	`sharing (VERB) --[ROOT]--> sharing (VERB)`
12900	`some (PRON) --[dobj]--> sharing (VERB)`
12901	`of (ADP) --[prep]--> some (PRON)`
12902	`what (PRON) --[dobj]--> stole (VERB)`
12903	`they (PRON) --[nsubj]--> stole (VERB)`
12904	`stole (VERB) --[pcomp]--> of (ADP)`
12905	`with (ADP) --[prep]--> stole (VERB)`
12906	`journalists (NOUN) --[pobj]--> with (ADP)`
12907	`, (PUNCT) --[punct]--> journalists (NOUN)`
12908	`including (VERB) --[prep]--> journalists (NOUN)`
12909	`research (NOUN) --[pobj]--> including (VERB)`
12910	`on (ADP) --[prep]--> research (NOUN)`
12911	`Trump (PROPN) --[poss]--> mate (NOUN)`
12912	`'s (PART) --[case]--> Trump (PROPN)`
12913	`running (NOUN) --[amod]--> mate (NOUN)`
12914	`mate (NOUN) --[pobj]--> on (ADP)`
12915	`, (PUNCT) --[punct]--> mate (NOUN)`
12916	`JD (PROPN) --[compound]--> Vance (PROPN)`
12917	`Vance (PROPN) --[appos]--> mate (NOUN)`
12918	`, (PUNCT) --[punct]--> Vance (PROPN)`
12919	`and (CCONJ) --[cc]--> Vance (PROPN)`
12920	`also (ADV) --[advmod]--> mate (NOUN)`
12921	`, (PUNCT) --[punct]--> sharing (VERB)`
12922	`most (ADV) --[advmod]--> recently (ADV)`
12923	`recently (ADV) --[advmod]--> letter (NOUN)`
12924	`, (PUNCT) --[punct]--> letter (NOUN)`
12925	`the (DET) --[det]--> letter (NOUN)`
12926	`September (PROPN) --[compound]--> letter (NOUN)`
12927	`letter (NOUN) --[npadvmod]--> sharing (VERB)`
12928	`from (ADP) --[prep]--> letter (NOUN)`
12929	`Trump (PROPN) --[poss]--> lawyer (NOUN)`
12930	`'s (PART) --[case]--> Trump (PROPN)`
12931	`lawyer (NOUN) --[pobj]--> from (ADP)`
12932	`to (ADP) --[prep]--> letter (NOUN)`
12933	`The (DET) --[det]--> Times (PROPN)`
12934	`New (PROPN) --[compound]--> York (PROPN)`
12935	`York (PROPN) --[compound]--> Times (PROPN)`
12936	`Times (PROPN) --[pobj]--> to (ADP)`
12937	`. (PUNCT) --[punct]--> sharing (VERB)`
12938
12939	`Sentence 7: Actually, today is the first time we've seen a journalist publish the Vance dossier in full.`
12940	`Actually (ADV) --[advmod]--> is (AUX)`
12941	`, (PUNCT) --[punct]--> is (AUX)`
12942	`today (NOUN) --[nsubj]--> is (AUX)`
12943	`is (AUX) --[ROOT]--> is (AUX)`
12944	`the (DET) --[det]--> time (NOUN)`
12945	`first (ADJ) --[amod]--> time (NOUN)`
12946	`time (NOUN) --[attr]--> is (AUX)`
12947	`we (PRON) --[nsubj]--> seen (VERB)`
12948	`'ve (AUX) --[aux]--> seen (VERB)`
12949	`seen (VERB) --[relcl]--> time (NOUN)`
12950	`a (DET) --[det]--> journalist (NOUN)`
12951	`journalist (NOUN) --[nsubj]--> publish (VERB)`
12952	`publish (VERB) --[ccomp]--> seen (VERB)`
12953	`the (DET) --[det]--> dossier (NOUN)`
12954	`Vance (PROPN) --[compound]--> dossier (NOUN)`
12955	`dossier (NOUN) --[dobj]--> publish (VERB)`
12956	`in (ADP) --[prep]--> publish (VERB)`
12957	`full (ADJ) --[amod]--> in (ADP)`
12958	`. (PUNCT) --[punct]--> is (AUX)`
12959
12960	`Sentence 8: It was on an independent Substack, and the argument was that it sheds light on the Trump campaign's perceptions of Vance's weaknesses, and, you know, the public deserves to know.`
12961	`It (PRON) --[nsubj]--> was (AUX)`
12962	`was (AUX) --[ROOT]--> was (AUX)`
12963	`on (ADP) --[prep]--> was (AUX)`
12964	`an (DET) --[det]--> Substack (PROPN)`
12965	`independent (ADJ) --[amod]--> Substack (PROPN)`
12966	`Substack (PROPN) --[pobj]--> on (ADP)`
12967	`, (PUNCT) --[punct]--> was (AUX)`
12968	`and (CCONJ) --[cc]--> was (AUX)`
12969	`the (DET) --[det]--> argument (NOUN)`
12970	`argument (NOUN) --[nsubj]--> was (AUX)`
12971	`was (AUX) --[conj]--> was (AUX)`
12972	`that (SCONJ) --[mark]--> sheds (VERB)`
12973	`it (PRON) --[nsubj]--> sheds (VERB)`
12974	`sheds (VERB) --[ccomp]--> was (AUX)`
12975	`light (NOUN) --[dobj]--> sheds (VERB)`
12976	`on (ADP) --[prep]--> sheds (VERB)`
12977	`the (DET) --[det]--> campaign (NOUN)`
12978	`Trump (PROPN) --[compound]--> campaign (NOUN)`
12979	`campaign (NOUN) --[poss]--> perceptions (NOUN)`
12980	`'s (PART) --[case]--> campaign (NOUN)`
12981	`perceptions (NOUN) --[pobj]--> on (ADP)`
12982	`of (ADP) --[prep]--> perceptions (NOUN)`
12983	`Vance (PROPN) --[poss]--> weaknesses (NOUN)`
12984	`'s (PART) --[case]--> Vance (PROPN)`
12985	`weaknesses (NOUN) --[pobj]--> of (ADP)`
12986	`, (PUNCT) --[punct]--> sheds (VERB)`
12987	`and (CCONJ) --[cc]--> sheds (VERB)`
12988	`, (PUNCT) --[punct]--> sheds (VERB)`
12989	`you (PRON) --[nsubj]--> know (VERB)`
12990	`know (VERB) --[parataxis]--> deserves (VERB)`
12991	`, (PUNCT) --[punct]--> deserves (VERB)`
12992	`the (DET) --[det]--> public (NOUN)`
12993	`public (NOUN) --[nsubj]--> deserves (VERB)`
12994	`deserves (VERB) --[conj]--> sheds (VERB)`
12995	`to (PART) --[aux]--> know (VERB)`
12996	`know (VERB) --[xcomp]--> deserves (VERB)`
12997	`. (PUNCT) --[punct]--> was (AUX)`
12998
12999	`Sentence 9: Though, the blogger, Ken Klippenstein, conceded that the information in the document is pretty much public already.`
13000	`Though (ADV) --[advmod]--> conceded (VERB)`
13001	`, (PUNCT) --[punct]--> conceded (VERB)`
13002	`the (DET) --[det]--> blogger (NOUN)`
13003	`blogger (NOUN) --[nsubj]--> conceded (VERB)`
13004	`, (PUNCT) --[punct]--> blogger (NOUN)`
13005	`Ken (PROPN) --[compound]--> Klippenstein (PROPN)`
13006	`Klippenstein (PROPN) --[appos]--> blogger (NOUN)`
13007	`, (PUNCT) --[punct]--> blogger (NOUN)`
13008	`conceded (VERB) --[ROOT]--> conceded (VERB)`
13009	`that (SCONJ) --[mark]--> is (AUX)`
13010	`the (DET) --[det]--> information (NOUN)`
13011	`information (NOUN) --[nsubj]--> is (AUX)`
13012	`in (ADP) --[prep]--> information (NOUN)`
13013	`the (DET) --[det]--> document (NOUN)`
13014	`document (NOUN) --[pobj]--> in (ADP)`
13015	`is (AUX) --[ccomp]--> conceded (VERB)`
13016	`pretty (ADV) --[advmod]--> much (ADV)`
13017	`much (ADV) --[advmod]--> public (ADJ)`
13018	`public (ADJ) --[acomp]--> is (AUX)`
13019	`already (ADV) --[advmod]--> is (AUX)`
13020	`. (PUNCT) --[punct]--> conceded (VERB)`
13021
13022	`Total sentences: 9`
13023
13024	`RAKE Keyphrase Relationships:`
13025	`noun phrase: "both campaigns" contains [campaigns], [campaigns]`
13026	`noun phrase: "Biden or Harris' campaigns" contains [harris], [campaigns], [campaigns], [biden]`
13027	`noun phrase: "Trump campaign emails" contains [trump campaign emails], [trump campaign], [trump], [trump]`
13028	`noun phrase: "Trump's running mate" contains [running mate], [trump], [trump]`
13029	`noun phrase: "JD Vance" contains [jd vance], [vance]`
13030	`noun phrase: "Trump's lawyer" contains [trump], [trump], [lawyer]`
13031	`noun phrase: "the Vance dossier" contains [vance dossier], [vance]`
13032	`noun phrase: "the Trump campaign's perceptions" contains [trump campaign], [trump], [trump], [perceptions]`
13033	`noun phrase: "Vance's weaknesses" contains [vance], [weaknesses]`
13034	`verb phrase: "targeting 've been campaigns" contains [targeting], [campaigns], [campaigns]`
13035	`verb phrase: "had success though with" contains [though], [though], [success]`
13036	`verb phrase: "sharing 've allegedly been some" contains [sharing], [allegedly]`
13037	`verb phrase: "know to" contains [know], [know]`
13038	`verb phrase: "conceded Though" contains [though], [though], [conceded]`
13039	`Total relationships found: 14`
13040
13041	`YAKE Keyphrase Relationships:`
13042	`noun phrase: "both campaigns" contains [campaigns], [campaign]`
13043	`noun phrase: "Biden or Harris' campaigns" contains [biden or harris'], [harris' campaigns], [campaigns], [campaign]`
13044	`noun phrase: "Trump campaign emails" contains [trump campaign], [trump], [trump campaign emails], [campaign], [campaign emails]`
13045	`noun phrase: "the Vance dossier" contains [vance], [vance dossier]`
13046	`noun phrase: "the Trump campaign's perceptions" contains [trump campaign], [trump], [campaign]`
13047	`verb phrase: "targeting 've been campaigns" contains [campaigns], [campaign]`
13048	`Total relationships found: 6`
13049
13050	`Timing Comparison:`
13051	`RAKE: 0.0000sec`
13052	`YAKE: 0.0350sec`
13053	`KeyBERT: 0.1058sec`
13054	`Dependencies: 0.0257sec`
13055	`Fastest: RAKE`
13056
13057	`================================================================================`
13058	`Message 94:`
13059	`MARTIN: ...In the way that some people who grew up with you knew this story. I didn't not remember or realize just how much pain you did go through. I mean, losing Paul Williams, who developed serious health problems, chronic alcohol problem, and then later took his own life - and then David Ruffin developed drug problems, and then Eddie Kendricks died of lung cancer. That had to have been awful.`
13060	`--------------------------------------------------------------------------------`
13061	`RAKE Keywords:`
13062	`- losing paul williams (score: 9.00) → lose Paul Williams`
13063	`- eddie kendricks died (score: 9.00) → Eddie Kendricks die`
13064	`- chronic alcohol problem (score: 9.00)`
13065	`- much pain (score: 4.00)`
13066	`- lung cancer (score: 4.00)`
13067	`- later took (score: 4.00) → later take`
13068	`- way (score: 1.00)`
13069	`- story (score: 1.00)`
13070	`- remember (score: 1.00)`
13071	`- realize (score: 1.00)`
13072	`- people (score: 1.00)`
13073	`- mean (score: 1.00)`
13074	`- martin (score: 1.00) → MARTIN`
13075	`- life (score: 1.00)`
13076	`- knew (score: 1.00) → know`
13077	`- grew (score: 1.00) → grow`
13078	`- go (score: 1.00)`
13079	`- awful (score: 1.00)`
13080	`- ... (score: 1.00)`
13081	`Total keywords: 19 extracted in 0.0000 seconds`
13082
13083	`YAKE Keywords:`
13084	`- knew this story (score: 0.04) → know this story`
13085	`- MARTIN (score: 0.05) → MARTIN`
13086	`- people who grew (score: 0.05) → people who grow`
13087	`- losing Paul Williams (score: 0.06) → lose Paul Williams`
13088	`- Paul Williams (score: 0.10) → Paul Williams`
13089	`- Eddie Kendricks died (score: 0.11) → Eddie Kendricks die`
13090	`- David Ruffin developed (score: 0.12) → David Ruffin develop`
13091	`- David Ruffin (score: 0.13) → David Ruffin`
13092	`- Eddie Kendricks (score: 0.13) → Eddie Kendricks`
13093	`- story (score: 0.16)`
13094	`- losing Paul (score: 0.20) → lose Paul`
13095	`- people (score: 0.22)`
13096	`- grew (score: 0.22) → grow`
13097	`- knew (score: 0.22) → know`
13098	`- problems (score: 0.23) → problem`
13099	`- Kendricks died (score: 0.25) → Kendricks die`
13100	`- Ruffin developed (score: 0.27) → Ruffin develop`
13101	`- Ruffin developed drug (score: 0.27) → Ruffin develop drug`
13102	`- Williams (score: 0.27) → Williams`
13103	`- chronic alcohol problem (score: 0.28)`
13104	`Total keywords: 20 extracted in 0.0258 seconds`
13105
13106	`KeyBERT Keywords:`
13107	`- eddie kendricks died (score: 0.58)`
13108	`- kendricks died lung (score: 0.54)`
13109	`- kendricks died (score: 0.51)`
13110	`- drug problems eddie (score: 0.48)`
13111	`- later took life (score: 0.48)`
13112	`- died lung cancer (score: 0.47)`
13113	`- problems eddie kendricks (score: 0.45)`
13114	`- took life david (score: 0.45)`
13115	`- died lung (score: 0.45)`
13116	`- life david ruffin (score: 0.43)`
13117	`- losing paul williams (score: 0.43)`
13118	`- took life (score: 0.42)`
13119	`- died (score: 0.41)`
13120	`- eddie kendricks (score: 0.41)`
13121	`- story didn remember (score: 0.40)`
13122	`- problems eddie (score: 0.39)`
13123	`- eddie (score: 0.38)`
13124	`- losing paul (score: 0.37)`
13125	`- realize just pain (score: 0.37)`
13126	`- paul williams (score: 0.37)`
13127	`Total keywords: 20 extracted in 0.0514 seconds`
13128
13129	`Dependency Relations (extracted in 0.0000sec):`
13130
13131	`Sentence 1: MARTIN: ...`
13132	`MARTIN (PROPN) --[ROOT]--> MARTIN (PROPN)`
13133	`: (PUNCT) --[punct]--> MARTIN (PROPN)`
13134	`... (PUNCT) --[punct]--> MARTIN (PROPN)`
13135
13136	`Sentence 2: In the way that some people who grew up with you knew this story.`
13137	`In (ADP) --[ROOT]--> In (ADP)`
13138	`the (DET) --[det]--> way (NOUN)`
13139	`way (NOUN) --[pobj]--> In (ADP)`
13140	`that (PRON) --[advmod]--> knew (VERB)`
13141	`some (DET) --[det]--> people (NOUN)`
13142	`people (NOUN) --[nsubj]--> knew (VERB)`
13143	`who (PRON) --[nsubj]--> grew (VERB)`
13144	`grew (VERB) --[relcl]--> people (NOUN)`
13145	`up (ADP) --[prt]--> grew (VERB)`
13146	`with (ADP) --[prep]--> grew (VERB)`
13147	`you (PRON) --[pobj]--> with (ADP)`
13148	`knew (VERB) --[relcl]--> way (NOUN)`
13149	`this (DET) --[det]--> story (NOUN)`
13150	`story (NOUN) --[dobj]--> knew (VERB)`
13151	`. (PUNCT) --[punct]--> In (ADP)`
13152
13153	`Sentence 3: I didn't not remember or realize just how much pain you did go through.`
13154	`I (PRON) --[nsubj]--> remember (VERB)`
13155	`did (AUX) --[aux]--> remember (VERB)`
13156	`n't (PART) --[neg]--> remember (VERB)`
13157	`not (PART) --[neg]--> remember (VERB)`
13158	`remember (VERB) --[ROOT]--> remember (VERB)`
13159	`or (CCONJ) --[cc]--> remember (VERB)`
13160	`realize (VERB) --[conj]--> remember (VERB)`
13161	`just (ADV) --[advmod]--> much (ADJ)`
13162	`how (SCONJ) --[advmod]--> much (ADJ)`
13163	`much (ADJ) --[amod]--> pain (NOUN)`
13164	`pain (NOUN) --[dobj]--> go (VERB)`
13165	`you (PRON) --[nsubj]--> go (VERB)`
13166	`did (AUX) --[aux]--> go (VERB)`
13167	`go (VERB) --[ccomp]--> realize (VERB)`
13168	`through (ADV) --[advmod]--> go (VERB)`
13169	`. (PUNCT) --[punct]--> remember (VERB)`
13170
13171	`Sentence 4: I mean, losing Paul Williams, who developed serious health problems, chronic alcohol problem, and then later took his own life - and then David Ruffin developed drug problems, and then Eddie Kendricks died of lung cancer.`
13172	`I (PRON) --[nsubj]--> mean (VERB)`
13173	`mean (VERB) --[ROOT]--> mean (VERB)`
13174	`, (PUNCT) --[punct]--> mean (VERB)`
13175	`losing (VERB) --[advcl]--> mean (VERB)`
13176	`Paul (PROPN) --[compound]--> Williams (PROPN)`
13177	`Williams (PROPN) --[dobj]--> losing (VERB)`
13178	`, (PUNCT) --[punct]--> Williams (PROPN)`
13179	`who (PRON) --[nsubj]--> developed (VERB)`
13180	`developed (VERB) --[relcl]--> Williams (PROPN)`
13181	`serious (ADJ) --[amod]--> problems (NOUN)`
13182	`health (NOUN) --[compound]--> problems (NOUN)`
13183	`problems (NOUN) --[dobj]--> developed (VERB)`
13184	`, (PUNCT) --[punct]--> Williams (PROPN)`
13185	`chronic (ADJ) --[amod]--> problem (NOUN)`
13186	`alcohol (NOUN) --[compound]--> problem (NOUN)`
13187	`problem (NOUN) --[appos]--> Williams (PROPN)`
13188	`, (PUNCT) --[punct]--> losing (VERB)`
13189	`and (CCONJ) --[cc]--> losing (VERB)`
13190	`then (ADV) --[advmod]--> took (VERB)`
13191	`later (ADV) --[advmod]--> took (VERB)`
13192	`took (VERB) --[conj]--> losing (VERB)`
13193	`his (PRON) --[poss]--> life (NOUN)`
13194	`own (ADJ) --[amod]--> life (NOUN)`
13195	`life (NOUN) --[dobj]--> took (VERB)`
13196	`- (PUNCT) --[punct]--> took (VERB)`
13197	`and (CCONJ) --[cc]--> took (VERB)`
13198	`then (ADV) --[advmod]--> developed (VERB)`
13199	`David (PROPN) --[compound]--> Ruffin (PROPN)`
13200	`Ruffin (PROPN) --[nsubj]--> developed (VERB)`
13201	`developed (VERB) --[conj]--> took (VERB)`
13202	`drug (NOUN) --[compound]--> problems (NOUN)`
13203	`problems (NOUN) --[dobj]--> developed (VERB)`
13204	`, (PUNCT) --[punct]--> developed (VERB)`
13205	`and (CCONJ) --[cc]--> developed (VERB)`
13206	`then (ADV) --[advmod]--> died (VERB)`
13207	`Eddie (PROPN) --[compound]--> Kendricks (PROPN)`
13208	`Kendricks (PROPN) --[nsubj]--> died (VERB)`
13209	`died (VERB) --[conj]--> developed (VERB)`
13210	`of (ADP) --[prep]--> died (VERB)`
13211	`lung (NOUN) --[compound]--> cancer (NOUN)`
13212	`cancer (NOUN) --[pobj]--> of (ADP)`
13213	`. (PUNCT) --[punct]--> mean (VERB)`
13214
13215	`Sentence 5: That had to have been awful.`
13216	`That (PRON) --[nsubj]--> had (VERB)`
13217	`had (VERB) --[ROOT]--> had (VERB)`
13218	`to (PART) --[aux]--> been (AUX)`
13219	`have (AUX) --[aux]--> been (AUX)`
13220	`been (AUX) --[xcomp]--> had (VERB)`
13221	`awful (ADJ) --[acomp]--> been (AUX)`
13222	`. (PUNCT) --[punct]--> had (VERB)`
13223
13224	`Total sentences: 5`
13225
13226	`RAKE Keyphrase Relationships:`
13227	`verb phrase: "knew that story" contains [story], [knew]`
13228	`Total relationships found: 1`
13229
13230	`YAKE Keyphrase Relationships:`
13231	`noun phrase: "Paul Williams" contains [paul williams], [williams]`
13232	`verb phrase: "knew that story" contains [story], [knew]`
13233	`Total relationships found: 2`
13234
13235	`Timing Comparison:`
13236	`RAKE: 0.0000sec`
13237	`YAKE: 0.0258sec`
13238	`KeyBERT: 0.0514sec`
13239	`Dependencies: 0.0000sec`
13240	`Fastest: RAKE`
13241
13242	`================================================================================`
13243	`Message 95:`
13244	`SHERLOCK: I asked that question to Ayman Mhanna. He's the executive director of the Samir Kassir Foundation, which works to promote freedom of expression. He says this is part of a pattern of growing repression in Lebanon.`
13245	`--------------------------------------------------------------------------------`
13246	`RAKE Keywords:`
13247	`- samir kassir foundation (score: 9.00) → Samir Kassir Foundation`
13248	`- promote freedom (score: 4.00)`
13249	`- growing repression (score: 4.00) → grow repression`
13250	`- executive director (score: 4.00)`
13251	`- ayman mhanna (score: 4.00) → Ayman Mhanna`
13252	`- works (score: 1.00) → work`
13253	`- sherlock (score: 1.00) → SHERLOCK`
13254	`- says (score: 1.00) → say`
13255	`- question (score: 1.00)`
13256	`- pattern (score: 1.00)`
13257	`- part (score: 1.00)`
13258	`- lebanon (score: 1.00) → Lebanon`
13259	`- expression (score: 1.00)`
13260	`- asked (score: 1.00) → ask`
13261	`Total keywords: 14 extracted in 0.0000 seconds`
13262
13263	`YAKE Keywords:`
13264	`- Ayman Mhanna (score: 0.00) → Ayman Mhanna`
13265	`- Samir Kassir Foundation (score: 0.01) → Samir Kassir Foundation`
13266	`- question to Ayman (score: 0.01) → question to Ayman`
13267	`- asked that question (score: 0.02) → ask that question`
13268	`- Kassir Foundation (score: 0.04) → Kassir Foundation`
13269	`- SHERLOCK (score: 0.04) → SHERLOCK`
13270	`- Samir Kassir (score: 0.04) → Samir Kassir`
13271	`- Mhanna (score: 0.05) → Mhanna`
13272	`- Ayman (score: 0.07) → Ayman`
13273	`- freedom of expression (score: 0.11)`
13274	`- repression in Lebanon (score: 0.11) → repression in Lebanon`
13275	`- asked (score: 0.14) → ask`
13276	`- question (score: 0.14)`
13277	`- executive director (score: 0.14)`
13278	`- works to promote (score: 0.14) → work to promote`
13279	`- promote freedom (score: 0.14)`
13280	`- Foundation (score: 0.16) → Foundation`
13281	`- Samir (score: 0.21) → Samir`
13282	`- Kassir (score: 0.21) → Kassir`
13283	`- Lebanon (score: 0.22) → Lebanon`
13284	`Total keywords: 20 extracted in 0.0198 seconds`
13285
13286	`KeyBERT Keywords:`
13287	`- repression lebanon (score: 0.64)`
13288	`- growing repression lebanon (score: 0.63)`
13289	`- lebanon (score: 0.48)`
13290	`- repression (score: 0.46)`
13291	`- ayman mhanna executive (score: 0.45)`
13292	`- growing repression (score: 0.45)`
13293	`- promote freedom expression (score: 0.43)`
13294	`- question ayman mhanna (score: 0.42)`
13295	`- asked question ayman (score: 0.40)`
13296	`- question ayman (score: 0.40)`
13297	`- ayman (score: 0.40)`
13298	`- freedom expression says (score: 0.40)`
13299	`- freedom expression (score: 0.39)`
13300	`- ayman mhanna (score: 0.39)`
13301	`- promote freedom (score: 0.39)`
13302	`- sherlock asked question (score: 0.38)`
13303	`- pattern growing repression (score: 0.37)`
13304	`- works promote freedom (score: 0.37)`
13305	`- sherlock (score: 0.36)`
13306	`- sherlock asked (score: 0.35)`
13307	`Total keywords: 20 extracted in 0.0279 seconds`
13308
13309	`Dependency Relations (extracted in 0.0197sec):`
13310
13311	`Sentence 1: SHERLOCK: I asked that question to Ayman Mhanna.`
13312	`SHERLOCK (PROPN) --[dep]--> asked (VERB)`
13313	`: (PUNCT) --[punct]--> asked (VERB)`
13314	`I (PRON) --[nsubj]--> asked (VERB)`
13315	`asked (VERB) --[ROOT]--> asked (VERB)`
13316	`that (DET) --[det]--> question (NOUN)`
13317	`question (NOUN) --[dobj]--> asked (VERB)`
13318	`to (ADP) --[prep]--> asked (VERB)`
13319	`Ayman (PROPN) --[compound]--> Mhanna (PROPN)`
13320	`Mhanna (PROPN) --[pobj]--> to (ADP)`
13321	`. (PUNCT) --[punct]--> asked (VERB)`
13322
13323	`Sentence 2: He's the executive director of the Samir Kassir Foundation, which works to promote freedom of expression.`
13324	`He (PRON) --[nsubj]--> 's (AUX)`
13325	`'s (AUX) --[ROOT]--> 's (AUX)`
13326	`the (DET) --[det]--> director (NOUN)`
13327	`executive (ADJ) --[amod]--> director (NOUN)`
13328	`director (NOUN) --[attr]--> 's (AUX)`
13329	`of (ADP) --[prep]--> director (NOUN)`
13330	`the (DET) --[det]--> Foundation (PROPN)`
13331	`Samir (PROPN) --[compound]--> Kassir (PROPN)`
13332	`Kassir (PROPN) --[compound]--> Foundation (PROPN)`
13333	`Foundation (PROPN) --[pobj]--> of (ADP)`
13334	`, (PUNCT) --[punct]--> Foundation (PROPN)`
13335	`which (PRON) --[nsubj]--> works (VERB)`
13336	`works (VERB) --[relcl]--> Foundation (PROPN)`
13337	`to (PART) --[aux]--> promote (VERB)`
13338	`promote (VERB) --[xcomp]--> works (VERB)`
13339	`freedom (NOUN) --[dobj]--> promote (VERB)`
13340	`of (ADP) --[prep]--> freedom (NOUN)`
13341	`expression (NOUN) --[pobj]--> of (ADP)`
13342	`. (PUNCT) --[punct]--> 's (AUX)`
13343
13344	`Sentence 3: He says this is part of a pattern of growing repression in Lebanon.`
13345	`He (PRON) --[nsubj]--> says (VERB)`
13346	`says (VERB) --[ROOT]--> says (VERB)`
13347	`this (PRON) --[nsubj]--> is (AUX)`
13348	`is (AUX) --[ccomp]--> says (VERB)`
13349	`part (NOUN) --[attr]--> is (AUX)`
13350	`of (ADP) --[prep]--> part (NOUN)`
13351	`a (DET) --[det]--> pattern (NOUN)`
13352	`pattern (NOUN) --[pobj]--> of (ADP)`
13353	`of (ADP) --[prep]--> pattern (NOUN)`
13354	`growing (VERB) --[amod]--> repression (NOUN)`
13355	`repression (NOUN) --[pobj]--> of (ADP)`
13356	`in (ADP) --[prep]--> repression (NOUN)`
13357	`Lebanon (PROPN) --[pobj]--> in (ADP)`
13358	`. (PUNCT) --[punct]--> says (VERB)`
13359
13360	`Total sentences: 3`
13361
13362	`RAKE Keyphrase Relationships:`
13363	`verb phrase: "asked question to" contains [question], [asked]`
13364	`Total relationships found: 1`
13365
13366	`YAKE Keyphrase Relationships:`
13367	`noun phrase: "Ayman Mhanna" contains [ayman mhanna], [mhanna], [ayman]`
13368	`noun phrase: "the Samir Kassir Foundation" contains [samir kassir foundation], [kassir foundation], [samir kassir], [foundation], [samir], [kassir]`
13369	`verb phrase: "asked question to" contains [asked], [question]`
13370	`Total relationships found: 3`
13371
13372	`Timing Comparison:`
13373	`RAKE: 0.0000sec`
13374	`YAKE: 0.0198sec`
13375	`KeyBERT: 0.0279sec`
13376	`Dependencies: 0.0197sec`
13377	`Fastest: RAKE`
13378
13379	`================================================================================`
13380	`Message 96:`
13381	`MEIKO NAKAHARA: (Singing in Japanese).`
13382	`--------------------------------------------------------------------------------`
13383	`RAKE Keywords:`
13384	`- meiko nakahara (score: 4.00) → MEIKO NAKAHARA`
13385	`- japanese ). (score: 4.00)`
13386	`- singing (score: 1.00) → sing`
13387	`Total keywords: 3 extracted in 0.0000 seconds`
13388
13389	`YAKE Keywords:`
13390	`- MEIKO NAKAHARA (score: 0.01) → MEIKO NAKAHARA`
13391	`- Singing in Japanese (score: 0.01) → sing in Japanese`
13392	`- MEIKO (score: 0.09) → MEIKO`
13393	`- NAKAHARA (score: 0.09) → NAKAHARA`
13394	`- Singing (score: 0.09) → sing`
13395	`- Japanese (score: 0.09) → Japanese`
13396	`Total keywords: 6 extracted in 0.0000 seconds`
13397
13398	`KeyBERT Keywords:`
13399	`- meiko nakahara singing (score: 0.91)`
13400	`- nakahara singing japanese (score: 0.90)`
13401	`- nakahara singing (score: 0.80)`
13402	`- singing japanese (score: 0.79)`
13403	`- meiko nakahara (score: 0.78)`
13404	`- meiko (score: 0.63)`
13405	`- nakahara (score: 0.60)`
13406	`- japanese (score: 0.52)`
13407	`- singing (score: 0.52)`
13408	`Total keywords: 9 extracted in 0.0115 seconds`
13409
13410	`Dependency Relations (extracted in 0.0137sec):`
13411
13412	`Sentence 1: MEIKO NAKAHARA: (Singing in Japanese).`
13413	`MEIKO (PROPN) --[compound]--> NAKAHARA (PROPN)`
13414	`NAKAHARA (PROPN) --[dep]--> Singing (VERB)`
13415	`: (PUNCT) --[punct]--> NAKAHARA (PROPN)`
13416	`( (PUNCT) --[punct]--> Singing (VERB)`
13417	`Singing (VERB) --[ROOT]--> Singing (VERB)`
13418	`in (ADP) --[prep]--> Singing (VERB)`
13419	`Japanese (PROPN) --[pobj]--> in (ADP)`
13420	`) (PUNCT) --[punct]--> Singing (VERB)`
13421	`. (PUNCT) --[punct]--> Singing (VERB)`
13422
13423	`Total sentences: 1`
13424
13425	`RAKE Keyphrase Relationships:`
13426	`No relationships found (no phrases contain 2+ RAKE keyphrases)`
13427
13428	`YAKE Keyphrase Relationships:`
13429	`No relationships found (no phrases contain 2+ YAKE keyphrases)`
13430
13431	`Timing Comparison:`
13432	`RAKE: 0.0000sec`
13433	`YAKE: 0.0000sec`
13434	`KeyBERT: 0.0115sec`
13435	`Dependencies: 0.0137sec`
13436	`Fastest: RAKE`
13437
13438	`================================================================================`
13439	`Message 97:`
13440	`CHANG: You see, she works an overnight shift as a security officer in Washington, D.C., and it is vital that she get to work on time - by 11 p.m. sharp.`
13441	`--------------------------------------------------------------------------------`
13442	`RAKE Keywords:`
13443	`- security officer (score: 4.00)`
13444	`- overnight shift (score: 4.00)`
13445	`- c ., (score: 4.00)`
13446	`- 11 p (score: 4.00)`
13447	`- works (score: 1.00) → work`
13448	`- work (score: 1.00)`
13449	`- washington (score: 1.00) → Washington`
13450	`- vital (score: 1.00)`
13451	`- time (score: 1.00)`
13452	`- sharp (score: 1.00)`
13453	`- see (score: 1.00)`
13454	`- get (score: 1.00)`
13455	`- chang (score: 1.00)`
13456	`Total keywords: 13 extracted in 0.0020 seconds`
13457
13458	`YAKE Keywords:`
13459	`- officer in Washington (score: 0.01) → officer in Washington`
13460	`- overnight shift (score: 0.03)`
13461	`- security officer (score: 0.03)`
13462	`- CHANG (score: 0.03)`
13463	`- sharp (score: 0.05)`
13464	`- Washington (score: 0.06) → Washington`
13465	`- work on time (score: 0.07)`
13466	`- works an overnight (score: 0.11) → work an overnight`
13467	`- time (score: 0.11)`
13468	`- overnight (score: 0.18)`
13469	`- shift (score: 0.18)`
13470	`- security (score: 0.18)`
13471	`- officer (score: 0.18)`
13472	`- vital (score: 0.18)`
13473	`- works (score: 0.26) → work`
13474	`- work (score: 0.26)`
13475	`Total keywords: 16 extracted in 0.0077 seconds`
13476
13477	`KeyBERT Keywords:`
13478	`- works overnight shift (score: 0.60)`
13479	`- shift security officer (score: 0.59)`
13480	`- overnight shift (score: 0.55)`
13481	`- security officer washington (score: 0.51)`
13482	`- overnight shift security (score: 0.51)`
13483	`- chang works overnight (score: 0.47)`
13484	`- security officer (score: 0.47)`
13485	`- vital work time (score: 0.45)`
13486	`- time 11 sharp (score: 0.44)`
13487	`- officer washington vital (score: 0.44)`
13488	`- work time 11 (score: 0.44)`
13489	`- officer washington (score: 0.42)`
13490	`- washington vital work (score: 0.41)`
13491	`- work time (score: 0.40)`
13492	`- shift security (score: 0.37)`
13493	`- officer (score: 0.36)`
13494	`- washington vital (score: 0.35)`
13495	`- works overnight (score: 0.34)`
13496	`- overnight (score: 0.34)`
13497	`- shift (score: 0.33)`
13498	`Total keywords: 20 extracted in 0.0286 seconds`
13499
13500	`Dependency Relations (extracted in 0.0060sec):`
13501
13502	`Sentence 1: CHANG: You see, she works an overnight shift as a security officer in Washington, D.C., and it is vital that she get to work on time - by 11 p.m. sharp.`
13503	`CHANG (NOUN) --[ROOT]--> CHANG (NOUN)`
13504	`: (PUNCT) --[punct]--> CHANG (NOUN)`
13505	`You (PRON) --[nsubj]--> see (VERB)`
13506	`see (VERB) --[parataxis]--> works (VERB)`
13507	`, (PUNCT) --[punct]--> works (VERB)`
13508	`she (PRON) --[nsubj]--> works (VERB)`
13509	`works (VERB) --[acl]--> CHANG (NOUN)`
13510	`an (DET) --[det]--> shift (NOUN)`
13511	`overnight (ADJ) --[amod]--> shift (NOUN)`
13512	`shift (NOUN) --[dobj]--> works (VERB)`
13513	`as (ADP) --[prep]--> shift (NOUN)`
13514	`a (DET) --[det]--> officer (NOUN)`
13515	`security (NOUN) --[compound]--> officer (NOUN)`
13516	`officer (NOUN) --[pobj]--> as (ADP)`
13517	`in (ADP) --[prep]--> officer (NOUN)`
13518	`Washington (PROPN) --[pobj]--> in (ADP)`
13519	`, (PUNCT) --[punct]--> Washington (PROPN)`
13520	`D.C. (PROPN) --[appos]--> Washington (PROPN)`
13521	`, (PUNCT) --[punct]--> works (VERB)`
13522	`and (CCONJ) --[cc]--> works (VERB)`
13523	`it (PRON) --[nsubj]--> is (AUX)`
13524	`is (AUX) --[conj]--> works (VERB)`
13525	`vital (ADJ) --[acomp]--> is (AUX)`
13526	`that (SCONJ) --[mark]--> get (VERB)`
13527	`she (PRON) --[nsubj]--> get (VERB)`
13528	`get (VERB) --[ccomp]--> is (AUX)`
13529	`to (PART) --[aux]--> work (VERB)`
13530	`work (VERB) --[xcomp]--> get (VERB)`
13531	`on (ADP) --[prep]--> work (VERB)`
13532	`time (NOUN) --[pobj]--> on (ADP)`
13533	`- (PUNCT) --[punct]--> work (VERB)`
13534	`by (ADP) --[prep]--> work (VERB)`
13535	`11 (NUM) --[nummod]--> p.m. (NOUN)`
13536	`p.m. (NOUN) --[npadvmod]--> sharp (ADJ)`
13537	`sharp (ADJ) --[amod]--> by (ADP)`
13538	`. (PUNCT) --[punct]--> is (AUX)`
13539
13540	`Total sentences: 1`
13541
13542	`RAKE Keyphrase Relationships:`
13543	`verb phrase: "works shift" contains [works], [work]`
13544	`Total relationships found: 1`
13545
13546	`YAKE Keyphrase Relationships:`
13547	`noun phrase: "an overnight shift" contains [overnight shift], [overnight], [shift]`
13548	`noun phrase: "a security officer" contains [security officer], [security], [officer]`
13549	`verb phrase: "works shift" contains [shift], [works], [work]`
13550	`Total relationships found: 3`
13551
13552	`Timing Comparison:`
13553	`RAKE: 0.0020sec`
13554	`YAKE: 0.0077sec`
13555	`KeyBERT: 0.0286sec`
13556	`Dependencies: 0.0060sec`
13557	`Fastest: RAKE`
13558
13559	`================================================================================`
13560	`Message 98:`
13561	`SUMMERS: He did.`
13562	`--------------------------------------------------------------------------------`
13563	`RAKE Keywords:`
13564	`- summers (score: 1.00) → summer`
13565	`Total keywords: 1 extracted in 0.0000 seconds`
13566
13567	`YAKE Keywords:`
13568	`- SUMMERS (score: 0.03) → summer`
13569	`Total keywords: 1 extracted in 0.0000`

microsoft/TypeAgent

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