Task
Assignment: Sentiment Analysis with Text Mining with R In Chapter 2 of Text Mining with R, the authors introduce sentiment analysis. Your task is to reproduce and extend that example.
Step 1 — Reproduce the Base Example Obtain the primary example code from Chapter 2.
Create a Quarto (.qmd) file that successfully runs this code.
Include a proper citation to the book and the original example code.
Step 2 — Extend the Analysis Extend the original example in two ways:
Use a different text corpus of your choosing (e.g., news articles, book reviews, speeches, social media, etc.).
Add at least one additional sentiment lexicon, which may come from:
Another R package, or
A lexicon you locate through external research.
You should clearly explain how the results differ from the original example.
Codebase
The code in Step 1 is from Silge and Robinson (2017), Chapter 2, ‘Sentiment analysis with tidy data’, https://www.tidytextmining.com/sentiment.html
Step 1: Reproduce the base example
The sentiments datasets
library (tidytext)library (janeaustenr)library (dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
library (stringr)library (ggplot2)library (syuzhet)get_sentiments ("nrc" )
# A tibble: 13,872 × 2
word sentiment
<chr> <chr>
1 abacus trust
2 abandon fear
3 abandon negative
4 abandon sadness
5 abandoned anger
6 abandoned fear
7 abandoned negative
8 abandoned sadness
9 abandonment anger
10 abandonment fear
# ℹ 13,862 more rows
# A tibble: 2,477 × 2
word value
<chr> <dbl>
1 abandon -2
2 abandoned -2
3 abandons -2
4 abducted -2
5 abduction -2
6 abductions -2
7 abhor -3
8 abhorred -3
9 abhorrent -3
10 abhors -3
# ℹ 2,467 more rows
# A tibble: 6,786 × 2
word sentiment
<chr> <chr>
1 2-faces negative
2 abnormal negative
3 abolish negative
4 abominable negative
5 abominably negative
6 abominate negative
7 abomination negative
8 abort negative
9 aborted negative
10 aborts negative
# ℹ 6,776 more rows
Sentiment analysis with inner join
<- austen_books () %>% group_by (book) %>% mutate (linenumber = row_number (),chapter = cumsum (str_detect (text, regex ("^chapter [ \\ divxlc]" , ignore_case = TRUE )))) %>% ungroup () %>% unnest_tokens (word, text)
<- get_sentiments ("nrc" ) %>% filter (sentiment == "joy" )%>% filter (book == "Emma" ) %>% inner_join (nrc_joy) %>% count (word, sort = TRUE )
Joining with `by = join_by(word)`
# A tibble: 301 × 2
word n
<chr> <int>
1 good 359
2 friend 166
3 hope 143
4 happy 125
5 love 117
6 deal 92
7 found 92
8 present 89
9 kind 82
10 happiness 76
# ℹ 291 more rows
library (tidyr)<- tidy_books %>% inner_join (get_sentiments ("bing" )) %>% count (book, index = linenumber %/% 80 , sentiment) %>% pivot_wider (names_from = sentiment, values_from = n, values_fill = 0 ) %>% mutate (sentiment = positive - negative)
Joining with `by = join_by(word)`
Warning in inner_join(., get_sentiments("bing")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 435434 of `x` matches multiple rows in `y`.
ℹ Row 5051 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.
library (ggplot2)ggplot (jane_austen_sentiment, aes (index, sentiment, fill = book)) + geom_col (show.legend = FALSE ) + facet_wrap (~ book, ncol = 2 , scales = "free_x" )
Comparing the three sentiment dictionaries
<- tidy_books %>% filter (book == "Pride & Prejudice" )
# A tibble: 122,204 × 4
book linenumber chapter word
<fct> <int> <int> <chr>
1 Pride & Prejudice 1 0 pride
2 Pride & Prejudice 1 0 and
3 Pride & Prejudice 1 0 prejudice
4 Pride & Prejudice 3 0 by
5 Pride & Prejudice 3 0 jane
6 Pride & Prejudice 3 0 austen
7 Pride & Prejudice 7 1 chapter
8 Pride & Prejudice 7 1 1
9 Pride & Prejudice 10 1 it
10 Pride & Prejudice 10 1 is
# ℹ 122,194 more rows
<- pride_prejudice %>% inner_join (get_sentiments ("afinn" )) %>% group_by (index = linenumber %/% 80 ) %>% summarise (sentiment = sum (value)) %>% mutate (method = "AFINN" )
Joining with `by = join_by(word)`
<- bind_rows (%>% inner_join (get_sentiments ("bing" )) %>% mutate (method = "Bing et al." ),%>% inner_join (get_sentiments ("nrc" ) %>% filter (sentiment %in% c ("positive" , "negative" ))%>% mutate (method = "NRC" )) %>% count (method, index = linenumber %/% 80 , sentiment) %>% pivot_wider (names_from = sentiment,values_from = n,values_fill = 0 ) %>% mutate (sentiment = positive - negative)
Joining with `by = join_by(word)`
Joining with `by = join_by(word)`
Warning in inner_join(., get_sentiments("nrc") %>% filter(sentiment %in% : Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 215 of `x` matches multiple rows in `y`.
ℹ Row 5178 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.
bind_rows (afinn, %>% ggplot (aes (index, sentiment, fill = method)) + geom_col (show.legend = FALSE ) + facet_wrap (~ method, ncol = 1 , scales = "free_y" )
get_sentiments ("nrc" ) %>% filter (sentiment %in% c ("positive" , "negative" )) %>% count (sentiment)
# A tibble: 2 × 2
sentiment n
<chr> <int>
1 negative 3316
2 positive 2308
get_sentiments ("bing" ) %>% count (sentiment)
# A tibble: 2 × 2
sentiment n
<chr> <int>
1 negative 4781
2 positive 2005
Most common positive and negative words
<- tidy_books %>% inner_join (get_sentiments ("bing" )) %>% count (word, sentiment, sort = TRUE ) %>% ungroup ()
Joining with `by = join_by(word)`
Warning in inner_join(., get_sentiments("bing")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 435434 of `x` matches multiple rows in `y`.
ℹ Row 5051 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.
# A tibble: 2,585 × 3
word sentiment n
<chr> <chr> <int>
1 miss negative 1855
2 well positive 1523
3 good positive 1380
4 great positive 981
5 like positive 725
6 better positive 639
7 enough positive 613
8 happy positive 534
9 love positive 495
10 pleasure positive 462
# ℹ 2,575 more rows
%>% group_by (sentiment) %>% slice_max (n, n = 10 ) %>% ungroup () %>% mutate (word = reorder (word, n)) %>% ggplot (aes (n, word, fill = sentiment)) + geom_col (show.legend = FALSE ) + facet_wrap (~ sentiment, scales = "free_y" ) + labs (x = "Contribution to sentiment" ,y = NULL )
<- bind_rows (tibble (word = c ("miss" ), lexicon = c ("custom" )),
# A tibble: 1,150 × 2
word lexicon
<chr> <chr>
1 miss custom
2 a SMART
3 a's SMART
4 able SMART
5 about SMART
6 above SMART
7 according SMART
8 accordingly SMART
9 across SMART
10 actually SMART
# ℹ 1,140 more rows
Loading required package: RColorBrewer
%>% anti_join (stop_words) %>% count (word) %>% with (wordcloud (word, n, max.words = 100 ))
Joining with `by = join_by(word)`
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
%>% inner_join (get_sentiments ("bing" )) %>% count (word, sentiment, sort = TRUE ) %>% acast (word ~ sentiment, value.var = "n" , fill = 0 ) %>% comparison.cloud (colors = c ("gray20" , "gray80" ),max.words = 100 )
Joining with `by = join_by(word)`
Warning in inner_join(., get_sentiments("bing")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 435434 of `x` matches multiple rows in `y`.
ℹ Row 5051 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.
Step 2: Extend the Analysis
2.1 Social Media Dataset
For the extended analysis, I will use the Social Media Sentiments Analysis Dataset from Kaggle,https://www.kaggle.com/datasets/kashishparmar02/social-media-sentiments-analysis-dataset . Unlike the original example, which analyzes Jane Austen’s novels, this dataset contains user-generated social media posts from different platforms, including Instagram, Facebook, and Twitter.
<- read.csv ("https://raw.githubusercontent.com/lhamo07/Data-607-Assignment/refs/heads/main/sentimentdataset.csv" )%>% rename_with (tolower) %>% mutate (post_id = row_number ())<- posts%>% # there is white space after each platform name so trim them before applying filter mutate (platform = str_trim (platform))%>% select (post_id,platform,text)%>% # format sentence into one word per row. unnest_tokens (word, text)
%>% filter (platform == "Instagram" ) %>% inner_join (nrc_joy, by = "word" ) %>% count (word, sort = TRUE )
word n
1 joy 11
2 feeling 10
3 laughter 9
4 art 8
5 beauty 8
6 surprise 8
7 garden 6
8 excitement 5
9 friend 5
10 hope 5
11 journey 5
12 optimism 5
13 serenity 5
14 celebration 4
15 confidence 4
16 dance 4
17 enthusiasm 4
18 glow 4
19 good 4
20 happiness 4
21 hopeful 4
22 love 4
23 victory 4
24 birthday 3
25 celebrating 3
26 dawn 3
27 delight 3
28 embrace 3
29 favorite 3
30 found 3
31 harmony 3
32 masterpiece 3
33 music 3
34 peace 3
35 pride 3
36 symphony 3
37 tranquil 3
38 whimsical 3
39 admiration 2
40 appreciation 2
41 beautiful 2
42 charity 2
43 create 2
44 endless 2
45 festival 2
46 friendship 2
47 gratitude 2
48 inspired 2
49 joyful 2
50 peaceful 2
51 reverence 2
52 thrill 2
53 treasure 2
54 triumph 2
55 weight 2
56 yearning 2
57 accomplished 1
58 achievement 1
59 amused 1
60 amusement 1
61 amusing 1
62 baby 1
63 blessed 1
64 blessings 1
65 bloom 1
66 childhood 1
67 choir 1
68 companion 1
69 completing 1
70 connoisseur 1
71 delicious 1
72 enthusiast 1
73 excited 1
74 food 1
75 football 1
76 freely 1
77 gem 1
78 gift 1
79 glimmer 1
80 grow 1
81 heal 1
82 hobby 1
83 immerse 1
84 independence 1
85 inspiration 1
86 luck 1
87 magical 1
88 orchestra 1
89 quaint 1
90 romance 1
91 share 1
92 special 1
93 success 1
94 sun 1
95 sunshine 1
96 thrilling 1
97 tranquility 1
98 unexpected 1
99 vacation 1
Using nrc_joy to count the number of joy word available in the instagram post. joy word appeared to be 11, feeling(10), laughter (9) and so on.
<- tidy_words %>% inner_join (get_sentiments ("bing" )) %>% count (platform,post_id, sentiment) %>% pivot_wider (names_from = sentiment, values_from = n, values_fill = 0 ) %>% mutate (sentiment = positive - negative)
Joining with `by = join_by(word)`
Warning in inner_join(., get_sentiments("bing")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 3314 of `x` matches multiple rows in `y`.
ℹ Row 2018 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.
inner_join() keeps only words that exist in the Bing lexicon. A post with no Bing-listed words has no rows after the join, so it never reaches count() and never appears in the result.
ggplot (social_media_sentiment, aes (post_id, sentiment, fill = platform)) + geom_col (show.legend = FALSE ) + facet_wrap (~ platform, ncol = 2 , scales = "free_x" )
Step 2: Analyze your social media dataset
2.2 Additional Sentiment Method: syuzhet
In addition to the three original sentiment lexicons, I used syuzhet lexicon, it gives sentiment to sentence rather than word.
get_sentiment_dictionary ("syuzhet" )
word value
1 abandon -0.75
2 abandoned -0.50
3 abandoner -0.25
4 abandonment -0.25
5 abandons -1.00
6 abducted -1.00
7 abduction -0.50
8 abductions -1.00
9 aberrant -0.60
10 aberration -0.80
11 abhor -0.50
12 abhorred -1.00
13 abhorrent -0.50
14 abhors -1.00
15 abilities 0.60
16 ability 0.50
17 abject -1.00
18 ablaze -0.25
19 abnormal -0.50
20 aboard 0.25
21 abolish -0.50
22 abominable -0.50
23 abominably -1.00
24 abominate -1.00
25 abomination -0.50
26 abort -0.50
27 aborted -0.80
28 abortion -0.80
29 abortive -1.00
30 aborts -0.60
31 abound 0.25
32 abounds 0.25
33 abrasion -0.60
34 abrasive -0.50
35 abrogate -0.40
36 abrupt -0.25
37 abruptly -0.25
38 abscess -0.50
39 abscond -1.00
40 absence -0.50
41 absent -0.60
42 absentee -0.75
43 absenteeism -1.00
44 absentees -0.60
45 absentminded -0.80
46 absolute -0.25
47 absolution 0.10
48 absolve 1.00
49 absolved 1.00
50 absolves 1.00
51 absolving 1.00
52 absorbed 0.50
53 abstinence -0.25
54 abstractedly -0.25
55 abstraction -0.25
56 absurd -0.75
57 absurdity -0.50
58 absurdly -0.80
59 absurdness -0.80
60 abundance 0.60
61 abundant 0.50
62 abuse -1.00
63 abused -0.75
64 abuses -0.50
65 abusive -0.50
66 abysmal -0.50
67 abysmally -1.00
68 abyss -0.75
69 academic 0.40
70 academy 0.40
71 accentuated 0.25
72 accept 1.00
73 acceptable 0.50
74 acceptance 0.80
75 accepted 0.80
76 accepting 0.60
77 accepts 0.80
78 accessable 0.80
79 accessible 0.75
80 accident -0.50
81 accidental -1.00
82 accidentally -0.60
83 accidents -0.60
84 acclaim 0.60
85 acclaimed 0.60
86 acclamation 0.60
87 accolade 0.50
88 accolades 0.60
89 accommodation 0.40
90 accommodative 0.80
91 accompaniment 0.60
92 accompany 0.40
93 accompanying 0.25
94 accomplish 0.75
95 accomplished 0.75
96 accomplishes 0.80
97 accomplishment 0.50
98 accomplishments 1.00
99 accord 0.80
100 accordingly 0.25
101 accost -0.80
102 accosted -0.80
103 accountability 0.80
104 accountable 0.80
105 accountant -0.25
106 accredited 0.80
107 accumulate 0.25
108 accurate 0.50
109 accurately 0.60
110 accursed -0.50
111 accusation -1.00
112 accusations -0.75
113 accusative -1.00
114 accuse -0.50
115 accused -0.50
116 accuser -1.00
117 accuses -0.50
118 accusing -1.00
119 accusingly -1.00
120 ace 0.80
121 acerbate -1.00
122 ache -0.75
123 ached -1.00
124 aches -0.50
125 achey -0.80
126 achievable 0.80
127 achieve 1.00
128 achievement 0.50
129 achievements 1.00
130 aching -0.75
131 achy -1.00
132 acid -0.40
133 acing 0.25
134 acknowledgment 0.40
135 acquire 0.80
136 acquired 0.80
137 acquiring 0.80
138 acquit -0.25
139 acquits -0.25
140 acquitted -0.25
141 acquitting -0.25
142 acrid -0.50
143 acridly -1.00
144 acridness -1.00
145 acrimonious -0.50
146 acrimoniously -1.00
147 acrimony -1.00
148 action 0.25
149 actionable 0.25
150 active 0.25
151 actual 0.40
152 actuality -0.25
153 acuity 0.60
154 acumen 0.50
155 acute -0.25
156 adapt 0.80
157 adaptable 0.50
158 adaptive 0.80
159 addict -1.00
160 addicted -0.50
161 addicting -0.80
162 addiction -1.00
163 addicts -1.00
164 additional 0.25
165 addled -0.60
166 addresses -0.25
167 adept 1.00
168 adequacy 0.80
169 adequate 0.50
170 adhered 0.60
171 adjudicate 0.40
172 adjunct -0.25
173 adjustable 0.40
174 adjustments 0.40
175 admirable 0.50
176 admirably 1.00
177 admiration 0.75
178 admire 1.00
179 admired 0.50
180 admirer 0.50
181 admires 1.00
182 admiring 0.75
183 admiringly 1.00
184 admissible 0.60
185 admit 0.60
186 admits 0.60
187 admitted 0.60
188 admonish -0.50
189 admonished -0.60
190 admonishingly -0.60
191 admonishment -0.60
192 admonition -0.50
193 adolescent 0.25
194 adopt 0.50
195 adopts 0.40
196 adorable 0.75
197 adorably 1.00
198 adoration 1.00
199 adore 0.75
200 adored 0.50
201 adorer 1.00
202 adores 0.50
203 adoring 1.00
204 adoringly 1.00
205 adorn 0.40
206 adorned 0.40
207 adrenaline -0.25
208 adrift -0.80
209 adroit 0.60
210 adroitly 0.60
211 adulate 0.25
212 adulation 0.25
213 adulatory 0.25
214 adulterate -1.00
215 adulterated -0.50
216 adulteration -1.00
217 adultery -1.00
218 advance 0.80
219 advanced 0.75
220 advancement 1.00
221 advances 0.80
222 advantage 0.75
223 advantageous 0.50
224 advantageously 1.00
225 advantages 0.75
226 advent 0.80
227 adventure 0.50
228 adventures 0.25
229 adventuresome 1.00
230 adventurous 0.75
231 adversarial -0.60
232 adversaries -0.60
233 adversary -0.75
234 adverse -0.50
235 adversity -0.50
236 advisable 1.00
237 advise 0.80
238 adviser 0.60
239 advocacy 0.60
240 advocate 0.60
241 advocated 0.60
242 advocates 0.60
243 aesthetic 0.25
244 aesthetics 0.25
245 affability 0.80
246 affable 0.50
247 affably 0.80
248 affectation -0.40
249 affectations -0.40
250 affection 1.00
251 affectionate 0.50
252 affiliated 0.25
253 affinity 0.80
254 affirm 0.50
255 affirmation 0.50
256 affirmative 0.50
257 affirmatively 0.80
258 afflict -0.50
259 afflicted -0.50
260 affliction -0.75
261 afflictive -1.00
262 affluence 0.50
263 affluent 0.50
264 afford 0.50
265 affordable 0.80
266 affordably 0.80
267 affront -0.75
268 affronted -1.00
269 aficionado 0.80
270 afraid -0.75
271 aftermath -0.50
272 aftertaste -0.40
273 ageless 0.60
274 aggravate -0.50
275 aggravated -0.75
276 aggravates -1.00
277 aggravating -0.75
278 aggravation -0.50
279 aggression -0.75
280 aggressions -1.00
281 aggressive -1.00
282 aggressively -1.00
283 aggressiveness -1.00
284 aggressor -0.50
285 aggrieved -0.50
286 aghast -1.00
287 agile 0.75
288 agilely 0.80
289 agility 0.75
290 agitated -0.50
291 agitation -1.00
292 aglow 0.80
293 agog -0.25
294 agonies -1.00
295 agonise -1.00
296 agonises -1.00
297 agonize -0.50
298 agonized -1.00
299 agonizes -1.00
300 agonizing -1.00
301 agonizingly -1.00
302 agony -0.50
303 agree 0.50
304 agreeable 0.75
305 agreeableness 1.00
306 agreeably 1.00
307 agreed 0.50
308 agreeing 1.00
309 agreement 0.50
310 agrees 0.50
311 aground -0.50
312 ahead 0.80
313 aid 1.00
314 aiding 0.10
315 ail -0.50
316 ailing -0.50
317 ailment -1.00
318 aimless -0.50
319 alarm -0.75
320 alarmed -0.75
321 alarming -0.75
322 alarmingly -0.60
323 alarmist -0.60
324 alarmists -0.60
325 alas -0.40
326 alcoholism -1.00
327 alert -0.25
328 alertness 0.60
329 alien -0.60
330 alienate -0.50
331 alienated -0.75
332 alienating -1.00
333 alienation -0.75
334 alimentation 0.60
335 alimony -0.40
336 alive 0.50
337 allay 0.60
338 allegation -0.50
339 allegations -0.80
340 allege -0.50
341 alleges -0.60
342 allegiance 0.50
343 allergic -0.50
344 allergies -0.50
345 allergy -1.00
346 alleviate 1.00
347 alleviation 1.00
348 allied 1.00
349 allies 0.25
350 allow 0.25
351 allowable 0.80
352 allure 0.80
353 alluring 0.50
354 alluringly 0.80
355 ally 0.80
356 almighty 1.00
357 aloha 0.40
358 alone -0.60
359 aloof -0.50
360 alter -0.25
361 altercation -0.75
362 altruistic 1.00
363 altruistically 1.00
364 amaze 0.50
365 amazed 0.50
366 amazement 0.80
367 amazes 0.50
368 amazing 0.50
369 amazingly 0.50
370 ambiguous -0.50
371 ambition 0.60
372 ambitions -0.25
373 ambitious 0.50
374 ambitiously 0.80
375 ambivalence -0.25
376 ambivalent -0.50
377 ambrosia 0.40
378 ambush -0.50
379 ameliorate 0.50
380 amen 0.60
381 amenable 0.50
382 amend 0.80
383 amends 0.80
384 amenity 0.25
385 amiability 1.00
386 amiable 0.50
387 amiably 0.80
388 amicability 1.00
389 amicable 0.50
390 amicably 1.00
391 amiss -1.00
392 amity -0.40
393 amnesia -0.60
394 amnesty 1.00
395 amour 0.80
396 amphetamines -0.40
397 ample 1.00
398 amply 1.00
399 amputate -1.00
400 amputated -1.00
401 amuse 0.75
402 amused 0.50
403 amusement 0.50
404 amusements 0.25
405 amusing 0.50
406 amusingly 0.80
407 anal -0.60
408 analyst 0.25
409 analyze 0.25
410 analyzing 0.25
411 anaphylactic -0.80
412 anarchism -0.50
413 anarchist -0.50
414 anarchistic -0.80
415 anarchy -0.50
416 anathema -1.00
417 ancestor 0.25
418 ancestors 0.25
419 ancient 0.25
420 anecdotes 0.40
421 anemic -1.00
422 angel 0.50
423 angelic 0.75
424 anger -0.75
425 angers -1.00
426 angina -0.25
427 angling 0.25
428 angrily -0.50
429 angriness -1.00
430 angry -0.75
431 angst -1.00
432 anguish -1.00
433 anguished -1.00
434 animalistic -0.80
435 animated 0.40
436 animation 0.25
437 animations 0.40
438 animosity -0.75
439 animus -0.40
440 annihilate -0.50
441 annihilated -1.00
442 annihilation -0.50
443 annoy -0.75
444 annoyance -1.00
445 annoyances -1.00
446 annoyed -0.50
447 annoying -0.75
448 annoyingly -1.00
449 annoys -0.50
450 annul -0.25
451 annulment -0.25
452 anointed 0.60
453 anomalous -0.25
454 anomaly -0.50
455 anonymity -0.25
456 antagonism -0.50
457 antagonist -0.75
458 antagonistic -0.75
459 antagonize -0.50
460 antediluvian -0.40
461 anthems 0.40
462 antibiotics 0.40
463 antichrist -1.00
464 anticipating -0.25
465 anticipation 0.25
466 antidote 0.80
467 antipathy -0.50
468 antiquated -0.50
469 antique 0.25
470 antiseptic 0.40
471 antisocial -1.00
472 antithesis -0.60
473 antithetical -0.40
474 antsy -0.80
475 anxieties -1.00
476 anxiety -1.00
477 anxious -0.75
478 anxiously -1.00
479 anxiousness -1.00
480 apathetic -0.75
481 apathetically -0.60
482 apathy -1.00
483 apeshit -0.80
484 aplomb 0.80
485 apocalypse -1.00
486 apocalyptic -0.50
487 apologetic 0.60
488 apologist -0.40
489 apologists -0.40
490 apologize 0.80
491 apologized 0.80
492 apologizes 0.80
493 apologizing 0.80
494 apology 0.60
495 apotheosis 0.80
496 appall -1.00
497 appalled -0.50
498 appalling -0.75
499 appallingly -1.00
500 appeal 0.60
501 appealing 0.50
502 appearing 0.25
503 appease 0.60
504 appeased 0.60
505 appeases 0.60
506 appeasing 0.60
507 applaud 0.50
508 applauded 0.80
509 applauding 0.80
510 applauds 0.50
511 applause 0.50
512 appraisal 0.25
513 appreciable 0.80
514 appreciate 0.50
515 appreciated 0.75
516 appreciates 0.75
517 appreciating 0.80
518 appreciation 0.75
519 appreciative 1.00
520 appreciatively 1.00
521 apprehends -0.80
522 apprehension -0.75
523 apprehensions -1.00
524 apprehensive -0.75
525 apprehensively -1.00
526 approbation 0.60
527 appropriate 1.00
528 appropriation -0.40
529 approval 1.00
530 approve 0.50
531 approved 1.00
532 approves 1.00
533 approving 1.00
534 apropos 0.60
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10040 unfulfilled -0.75
10041 unfunded -0.25
10042 unfurnished -0.60
10043 ungainly -1.00
10044 ungentlemanly -0.60
10045 unglued -0.60
10046 ungodly -1.00
10047 ungovernable -0.80
10048 ungrateful -0.50
10049 unhappily -1.00
10050 unhappiness -0.50
10051 unhappy -0.75
10052 unhealthy -0.75
10053 unheard -0.60
10054 unhelpful -1.00
10055 unhinged -0.80
10056 unholy -0.50
10057 unification 0.40
10058 unified 0.60
10059 unify 1.00
10060 unimaginable -0.50
10061 unimaginably -0.60
10062 unimportant -0.50
10063 unimpressed -0.50
10064 unimpressive -0.80
10065 unimproved -0.80
10066 uninfected 0.25
10067 uninformed -0.50
10068 uninitiated -0.60
10069 uninjured 0.40
10070 uninspired -0.50
10071 uninspiring -1.00
10072 uninsured -0.80
10073 unintelligent -1.00
10074 unintelligible -0.50
10075 unintentionally -0.40
10076 uninterested -0.80
10077 uninteresting -0.50
10078 unique 0.60
10079 unison 0.40
10080 unitary 0.25
10081 unite 0.60
10082 united 0.50
10083 unity 0.50
10084 universal 0.40
10085 university 0.60
10086 unjust -0.75
10087 unjustifiable -0.50
10088 unjustifiably -0.80
10089 unjustified -0.50
10090 unjustly -1.00
10091 unkind -0.50
10092 unkindly -1.00
10093 unknowable -0.80
10094 unknown -0.50
10095 unlawful -0.50
10096 unlawfully -1.00
10097 unlawfulness -1.00
10098 unleash -0.25
10099 unlicensed -0.50
10100 unlikely -0.50
10101 unlimited 0.50
10102 unlovable -0.75
10103 unloved -0.50
10104 unlucky -0.50
10105 unmanageable -1.00
10106 unmarked -0.25
10107 unmatched 0.50
10108 unmoored -0.25
10109 unmotivated -1.00
10110 unmoved -0.80
10111 unnatural -0.75
10112 unnaturally -0.80
10113 unnecessary -0.60
10114 unneeded -0.60
10115 unnerve -1.00
10116 unnerved -0.50
10117 unnerving -1.00
10118 unnervingly -1.00
10119 unnoticed -0.50
10120 unobserved -0.25
10121 unofficial -0.10
10122 unorthodox -0.25
10123 unorthodoxy -0.60
10124 unpaid -0.80
10125 unpainted -0.10
10126 unpleasant -0.75
10127 unpleasantries -0.80
10128 unpopular -0.50
10129 unpredictable -0.50
10130 unprepared -0.75
10131 unprocessed -0.25
10132 unproductive -0.50
10133 unprofessional -1.00
10134 unprofitable -0.50
10135 unpronounceable -0.80
10136 unprotected -0.80
10137 unproved -0.25
10138 unproven -0.80
10139 unpublished -0.25
10140 unqualified -0.50
10141 unquestionable 0.50
10142 unquestionably 0.50
10143 unquestioned -0.25
10144 unravel -0.10
10145 unraveled -0.50
10146 unraveling -0.10
10147 unreachable -0.50
10148 unreadable -0.80
10149 unreal -0.40
10150 unrealistic -1.00
10151 unreasonable -0.50
10152 unreasonably -0.50
10153 unrecognizable -0.60
10154 unrecovered -0.80
10155 unrelenting -0.40
10156 unrelentingly -0.40
10157 unreliability -1.00
10158 unreliable -0.75
10159 unrequited -0.60
10160 unresolved -1.00
10161 unresponsive -1.00
10162 unrest -1.00
10163 unrestricted -0.25
10164 unrivaled 0.40
10165 unruly -0.50
10166 unsafe -0.50
10167 unsaid -0.25
10168 unsatisfactory -0.50
10169 unsatisfiable -1.00
10170 unsatisfied -0.50
10171 unsatisfying -1.00
10172 unsavory -0.75
10173 unscathed 0.80
10174 unscheduled -0.25
10175 unscrupulous -0.50
10176 unscrupulously -1.00
10177 unsecure -0.25
10178 unsecured -0.80
10179 unseemly -1.00
10180 unselfish 0.50
10181 unsettle -1.00
10182 unsettled -0.75
10183 unsettling -1.00
10184 unsettlingly -1.00
10185 unshakable 0.25
10186 unsightly -1.00
10187 unskilled -1.00
10188 unsmiling -1.00
10189 unsophisticated -0.75
10190 unsound -1.00
10191 unspeakable -0.75
10192 unspeakably -0.80
10193 unspecified -0.40
10194 unstable -1.00
10195 unsteadily -0.80
10196 unsteadiness -0.80
10197 unsteady -0.80
10198 unstoppable -0.25
10199 unsuccessful -0.50
10200 unsuccessfully -1.00
10201 unsuitable -0.80
10202 unsung -0.25
10203 unsupported -0.75
10204 unsupportive -0.80
10205 unsure -0.50
10206 unsurpassed 0.40
10207 unsurprising -0.25
10208 unsuspecting -0.50
10209 unsustainable -0.50
10210 unsympathetic -0.50
10211 untamed -0.60
10212 untarnished 0.40
10213 untenable -0.50
10214 untested -0.40
10215 unthinkable -0.75
10216 unthinkably -0.80
10217 untidy -1.00
10218 untimely -0.50
10219 untold -0.25
10220 untouchable -0.25
10221 untouched -0.50
10222 untoward -0.80
10223 untrained -0.80
10224 untrue -0.50
10225 untrustworthy -0.50
10226 untruthful -1.00
10227 unusable -0.80
10228 unusably -0.80
10229 unusual -0.60
10230 unusually -0.60
10231 unveil 0.25
10232 unveiling -0.25
10233 unwanted -0.75
10234 unwarranted -0.50
10235 unwashed -0.60
10236 unwatchable -1.00
10237 unwavering 0.50
10238 unwelcome -0.75
10239 unwell -0.50
10240 unwieldy -1.00
10241 unwilling -0.80
10242 unwillingly -0.80
10243 unwillingness -0.50
10244 unwise -0.50
10245 unwisely -1.00
10246 unwitting -0.80
10247 unworkable -0.80
10248 unworthy -0.75
10249 unyielding -0.50
10250 upbeat 1.00
10251 upbraid -0.80
10252 upcoming 0.40
10253 upgraded 0.80
10254 upheaval -0.50
10255 upheld 0.60
10256 uphold 0.60
10257 uplift 0.50
10258 uplifting 1.00
10259 upright 0.60
10260 uprising -0.50
10261 uproar -0.50
10262 uproarious 0.25
10263 uproot -0.60
10264 uprooted -0.60
10265 upscale 0.40
10266 upset -0.75
10267 upsets -0.50
10268 upsetting -0.80
10269 upsettingly -1.00
10270 upstaged -0.60
10271 uptight -0.50
10272 urchin -0.25
10273 urgent -0.75
10274 urinals -0.25
10275 urine -0.60
10276 usable 0.60
10277 useable 0.60
10278 useful 0.50
10279 usefulness 1.00
10280 useless -0.75
10281 uselessness -0.60
10282 userfriendly 1.00
10283 usual 0.40
10284 usurp -0.50
10285 usurped -0.80
10286 usurper -0.80
10287 usury -1.00
10288 utility 0.40
10289 utmost 0.40
10290 utopian 0.80
10291 uttered -0.25
10292 uttering -0.25
10293 utterly -0.40
10294 utters -0.25
10295 vacation 0.80
10296 vacations 0.80
10297 vacuous -1.00
10298 vaginal -0.25
10299 vagrant -0.50
10300 vague -0.75
10301 vagueness -0.50
10302 vain -1.00
10303 vainly -0.50
10304 valedictorian 0.25
10305 valiant 0.50
10306 valiantly 1.00
10307 validate 0.80
10308 validated 0.80
10309 validates 0.80
10310 validating 0.80
10311 valleys 0.25
10312 valor 0.50
10313 valuable 0.50
10314 vampire -0.10
10315 vanguard 0.25
10316 vanished -0.60
10317 vanishing -0.60
10318 vanity -0.50
10319 vanquish -0.60
10320 varicose -0.80
10321 variety 0.25
10322 vegetative -0.25
10323 vehement -0.25
10324 vehemently -0.25
10325 velvety 0.60
10326 vendetta -0.80
10327 venerable 0.60
10328 venerate 0.60
10329 veneration 0.50
10330 vengeance -0.75
10331 vengeful -0.75
10332 vengefully -1.00
10333 vengefulness -1.00
10334 venom -0.75
10335 venomous -0.50
10336 venomously -1.00
10337 vent -0.25
10338 venting -0.25
10339 veracity 0.80
10340 verbosity -0.25
10341 verdant 0.80
10342 verifiable 0.60
10343 verification 0.60
10344 verified 0.50
10345 verify 0.60
10346 verily 0.80
10347 veritable 0.50
10348 vermin -1.00
10349 vernal 0.25
10350 versatile 1.00
10351 versatility 1.00
10352 versus -0.25
10353 vertigo -0.50
10354 verve 0.60
10355 vestiges -0.25
10356 veteran 0.10
10357 veto -0.25
10358 vex -1.00
10359 vexation -0.50
10360 vexing -0.50
10361 vexingly -1.00
10362 viable 0.25
10363 vibrant 0.75
10364 vibrantly 1.00
10365 vice -0.50
10366 vicious -0.75
10367 viciously -1.00
10368 viciousness -1.00
10369 victim -0.75
10370 victimize -0.75
10371 victimized -0.75
10372 victimizes -1.00
10373 victimizing -1.00
10374 victims -0.75
10375 victor 0.50
10376 victorious 0.50
10377 victory 0.75
10378 viewable 0.25
10379 vigilance 0.50
10380 vigilant 0.75
10381 vigor 0.50
10382 vigorous 0.50
10383 vile -0.75
10384 vileness -1.00
10385 vilify -0.40
10386 villager -0.10
10387 villagers -0.25
10388 villain -1.00
10389 villainous -1.00
10390 villainously -1.00
10391 villains -1.00
10392 vindicate 0.25
10393 vindicated 0.50
10394 vindicates 0.25
10395 vindicating 0.25
10396 vindication 0.25
10397 vindictive -0.50
10398 vindictively -0.25
10399 vindictiveness -0.50
10400 violate -0.50
10401 violated -1.00
10402 violates -1.00
10403 violating -0.50
10404 violation -0.50
10405 violator -1.00
10406 violators -1.00
10407 violence -0.50
10408 violent -0.75
10409 violently -0.75
10410 viper -0.25
10411 virgin 0.40
10412 virginity 0.50
10413 virtue 0.50
10414 virtuous 0.75
10415 virtuously 1.00
10416 virulence -0.50
10417 virulent -0.50
10418 virulently -1.00
10419 virus -0.50
10420 vision 0.50
10421 visionary 0.75
10422 visit 0.40
10423 visitation -0.25
10424 visitor 0.25
10425 vital 0.40
10426 vitality 0.50
10427 vitriolic -1.00
10428 vivacious 0.75
10429 vivid 0.50
10430 vixen -0.25
10431 vociferous -0.25
10432 vociferously -0.25
10433 vogue 0.60
10434 voided -0.40
10435 voids -0.40
10436 volatile -0.40
10437 volatility -0.50
10438 volunteer 0.80
10439 voluptuous 0.60
10440 vomit -1.00
10441 vomited -0.50
10442 vomiting -0.50
10443 vomits -0.50
10444 voodoo -0.25
10445 voter 0.60
10446 vouchsafe 0.60
10447 vow 0.60
10448 vows 0.60
10449 vulgar -0.50
10450 vulgarity -1.00
10451 vulnerability -0.50
10452 vulnerable -0.50
10453 vulture -0.25
10454 wacko -1.00
10455 waft -0.25
10456 wages 0.40
10457 wail -0.75
10458 wailing -1.00
10459 wait -0.25
10460 wallow -0.75
10461 waltz 0.60
10462 waning -0.40
10463 wanton -0.80
10464 war -0.50
10465 warden -0.40
10466 wardens -0.40
10467 wardrobes 0.40
10468 wards -0.40
10469 warfare -0.50
10470 warily -1.00
10471 wariness -1.00
10472 warlike -0.50
10473 warm 0.50
10474 warmer 0.60
10475 warmhearted 1.00
10476 warmly 1.00
10477 warms 0.60
10478 warmth 0.50
10479 warn -0.75
10480 warned -0.50
10481 warning -0.50
10482 warnings -0.50
10483 warns -0.25
10484 warp -0.25
10485 warped -0.50
10486 warranty 0.25
10487 warring -1.00
10488 warrior -0.60
10489 wars -1.00
10490 wart -0.60
10491 wary -1.00
10492 washedout -0.25
10493 waste -0.75
10494 wasted -0.75
10495 wasteful -0.50
10496 wastefulness -1.00
10497 wasting -1.00
10498 watchdog -0.25
10499 watchful 0.60
10500 watchman -0.40
10501 waterdown -0.25
10502 watereddown -0.25
10503 waterlogged -0.80
10504 waterproof 0.60
10505 watery -0.25
10506 waver -0.80
10507 wavering -0.80
10508 wayward -0.80
10509 weak -0.50
10510 weaken -0.50
10511 weakened -1.00
10512 weakening -0.50
10513 weaker -1.00
10514 weakly -1.00
10515 weakness -1.00
10516 weaknesses -1.00
10517 wealth 0.50
10518 wealthy 0.50
10519 wearily -1.00
10520 weariness -0.75
10521 wearisome -1.00
10522 weary -0.75
10523 weatherproof 0.60
10524 weddings 0.60
10525 wedge -0.25
10526 weed -0.25
10527 weeds -0.25
10528 weep -1.00
10529 weeping -0.50
10530 weepy -1.00
10531 weird -0.25
10532 weirder -0.80
10533 weirdly -0.80
10534 weirdo -1.00
10535 welcome 0.50
10536 welcomed 0.75
10537 welcomes 1.00
10538 well 0.80
10539 wellbalanced 1.00
10540 wellbehaved 1.00
10541 wellbeing 1.00
10542 wellbred 1.00
10543 wellconnected 1.00
10544 welleducated 1.00
10545 wellestablished 1.00
10546 wellinformed 1.00
10547 wellintentioned 1.00
10548 wellknown 1.00
10549 wellmade 1.00
10550 wellmanaged 1.00
10551 wellmannered 1.00
10552 wellpositioned 1.00
10553 wellreceived 1.00
10554 wellregarded 1.00
10555 wellrounded 1.00
10556 wellrun 1.00
10557 wellwishers 1.00
10558 welts -1.00
10559 wench -1.00
10560 wheedle -0.25
10561 wheelchair -0.25
10562 wheezing -1.00
10563 whim -0.25
10564 whimper -1.00
10565 whimpered -1.00
10566 whims 0.40
10567 whimsical 0.40
10568 whine -0.25
10569 whines -1.00
10570 whining -1.00
10571 whiny -1.00
10572 whip -0.25
10573 whipped -0.80
10574 whips -0.25
10575 whisperings -0.40
10576 white 0.40
10577 whitened 0.40
10578 whiteness 0.40
10579 wholeheartedly 1.00
10580 wholeness 1.00
10581 wholesome 0.50
10582 whoop 0.40
10583 whooping 0.40
10584 whore -1.00
10585 whores -0.50
10586 wicca -0.25
10587 wicked -0.75
10588 wickedly -1.00
10589 wickedness -0.50
10590 widespread -0.25
10591 widowed -1.00
10592 wild -0.25
10593 wildfire -0.25
10594 willful -0.25
10595 willies -0.25
10596 willing 0.80
10597 willingly 0.50
10598 willingness 0.75
10599 wilt -0.25
10600 wilted -1.00
10601 wilting -1.00
10602 wimp -1.00
10603 wimpy -0.25
10604 win 0.50
10605 wince -0.75
10606 winced -1.00
10607 windfall 0.50
10608 windpipe -0.25
10609 winked 0.60
10610 winking 0.60
10611 winks 0.60
10612 winnable 0.80
10613 winner 0.75
10614 winners 0.80
10615 winning 0.75
10616 winningly 1.00
10617 winnings 1.00
10618 wins 0.50
10619 wireless 0.25
10620 wisdom 0.75
10621 wise 0.50
10622 wisecrack -0.40
10623 wisely 0.50
10624 wish 0.80
10625 wishes 0.80
10626 wishing 0.80
10627 wit 0.60
10628 witch -0.40
10629 witchcraft -0.25
10630 withdraw -0.80
10631 withdrawn -0.80
10632 wither -1.00
10633 withered -1.00
10634 withering -1.00
10635 withstand 0.25
10636 witnessing -0.25
10637 wits 0.60
10638 witty 0.50
10639 wizard 0.40
10640 wobbled -0.60
10641 wobbles -0.60
10642 wobbly -0.60
10643 woe -0.50
10644 woebegone -0.50
10645 woeful -0.75
10646 woefully -0.50
10647 wolf -0.25
10648 womanizer -0.50
10649 womanizing -1.00
10650 womanly -0.25
10651 womb 0.25
10652 won 0.50
10653 wonderful 0.75
10654 wonderfully 0.75
10655 wonderment 1.00
10656 wondrous 1.00
10657 wondrously 1.00
10658 wooziness -0.25
10659 work 0.25
10660 workable 0.40
10661 worked 0.25
10662 working 0.25
10663 works 0.25
10664 worldfamous 1.00
10665 worm -0.25
10666 worn -0.10
10667 worried -0.75
10668 worriedly -0.80
10669 worrier -0.25
10670 worries -0.50
10671 worrisome -1.00
10672 worry -0.75
10673 worrying -0.75
10674 worryingly -1.00
10675 worse -0.75
10676 worsen -0.50
10677 worsened -1.00
10678 worsening -1.00
10679 worsens -1.00
10680 worship 0.25
10681 worshipped 0.25
10682 worshipping 0.25
10683 worships 0.25
10684 worst -0.50
10685 worth 0.75
10686 worthiness 1.00
10687 worthless -1.00
10688 worthlessly -1.00
10689 worthlessness -1.00
10690 worthwhile 1.00
10691 worthy 0.75
10692 wound -0.50
10693 wounds -1.00
10694 wow 0.50
10695 wowed 1.00
10696 wrath -0.50
10697 wrathful -1.00
10698 wreak -0.50
10699 wreaked -1.00
10700 wreaks -1.00
10701 wreck -0.75
10702 wrecked -1.00
10703 wreckers -1.00
10704 wrench -0.50
10705 wrest -0.60
10706 wrestle -0.40
10707 wrestled -0.40
10708 wrestling -0.40
10709 wretch -0.50
10710 wretched -0.50
10711 wretchedly -1.00
10712 wretchedness -0.50
10713 wrinkle -0.25
10714 wrinkled -0.40
10715 wrinkles -0.50
10716 writhe -0.80
10717 writhing -0.80
10718 wrong -0.75
10719 wrongdoing -1.00
10720 wronged -0.50
10721 wrongful -0.50
10722 wrongly -0.50
10723 wrought -0.50
10724 wrung -0.25
10725 wry -0.25
10726 xenophobia -0.25
10727 yanked -0.40
10728 yanks -0.40
10729 yawn -0.25
10730 yell -0.25
10731 yelp -0.80
10732 yelped -0.60
10733 yes 0.80
10734 young 0.40
10735 younger 0.25
10736 youth 0.40
10737 youthful 0.50
10738 yowl -0.40
10739 yummy 0.80
10740 zeal 0.50
10741 zealot -0.25
10742 zealous 0.40
10743 zenith 0.40
10744 zest 0.50
10745 zombie -0.25
10746 zombies -0.25
10747 false -0.60
10748 true 0.50
<- posts %>% mutate (platform = str_trim (platform),sentiment = get_sentiment (text, method = "syuzhet" )%>% select (post_id, platform, text, sentiment)
ggplot (syuzhet_sentiment, aes (post_id, sentiment, fill = platform)) + geom_col (show.legend = FALSE ) + facet_wrap (~ platform, ncol = 2 , scales = "free_x" )
Comparing the four sentiment dictionaries
<- tidy_words %>% filter (platform == "Instagram" )
<- syuzhet_sentiment %>% filter (platform == "Instagram" )
<- instagram_data %>% inner_join (get_sentiments ("afinn" ), by = "word" ) %>% group_by (post_id) %>% summarise (sentiment = sum (value), .groups = "drop" ) %>% mutate (method = "AFINN" )
<- instagram_data %>% inner_join (get_sentiments ("bing" ), by = "word" ) %>% count (post_id, sentiment) %>% pivot_wider (names_from = sentiment,values_from = n,values_fill = 0 %>% mutate (sentiment = positive - negative) %>% select (post_id, sentiment) %>% mutate (method = "Bing" )
<- instagram_data %>% inner_join (get_sentiments ("nrc" ) %>% filter (sentiment %in% c ("positive" , "negative" )),by = "word" %>% count (post_id, sentiment) %>% pivot_wider (names_from = sentiment,values_from = n,values_fill = 0 %>% mutate (sentiment = positive - negative) %>% select (post_id, sentiment) %>% mutate (method = "NRC" )
Warning in inner_join(., get_sentiments("nrc") %>% filter(sentiment %in% : Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 46 of `x` matches multiple rows in `y`.
ℹ Row 1998 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.
<- instagram_posts %>% mutate (sentiment = get_sentiment (text, method = "syuzhet" ),method = "Syuzhet" %>% select (post_id, sentiment, method)
<- bind_rows (
ggplot (all_sentiments,aes (x = post_id, y = sentiment, color = method)) + geom_col (show.legend = FALSE ) + facet_wrap (~ method, ncol = 2 , scales = "free_y" ) + labs (title = "Comparison of Sentiment Lexicons for Instagram Posts" ,x = "Post ID" ,y = "Sentiment Score" + theme_minimal ()
The four sentiment methods generate very different absolute scores, but they follow almost the same relative pattern across the Instagram timeline, similar to what was observed in Pride and Prejudice novel. However, the Instagram sentiment chart shows much greater fluctuations, with frequent changes in sentiment compared to the more gradual peaks and dips in the novel.
In Pride and Prejudice, AFINN scores peaked as high as +80. In contrast, on Instagram, AFINN’s highest peak barely exceeds +10, while Bing and NRC peak around +6.
In the previous chart, the NRC lexicon remained almost entirely above the zero line, indicating a strong positive baseline shift. However, in the Instagram dataset, NRC scores drop significantly below zero, following the negative dips observed in the other lexicons, particularly around Post 300.
The Syuzhet lexicon uses a much narrower scoring range, approximately -2.5 to +2.5, because it assigns decimal weights to words rather than whole-number scores. Despite this smaller scale, it closely follows the same overall patterns and movements as AFINN and Bing.
<- tidy_words %>% inner_join (get_sentiments ("bing" )) %>% count (word, sentiment, sort = TRUE ) %>% ungroup ()
Joining with `by = join_by(word)`
Warning in inner_join(., get_sentiments("bing")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 3314 of `x` matches multiple rows in `y`.
ℹ Row 2018 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.
# A tibble: 2,585 × 3
word sentiment n
<chr> <chr> <int>
1 miss negative 1855
2 well positive 1523
3 good positive 1380
4 great positive 981
5 like positive 725
6 better positive 639
7 enough positive 613
8 happy positive 534
9 love positive 495
10 pleasure positive 462
# ℹ 2,575 more rows
%>% group_by (sentiment) %>% slice_max (n, n = 10 ) %>% ungroup () %>% mutate (word = reorder (word, n)) %>% ggplot (aes (n, word, fill = sentiment)) + geom_col (show.legend = FALSE ) + facet_wrap (~ sentiment, scales = "free_y" ) + labs (x = "Contribution to sentiment" ,y = NULL )
In the austen_books the most common positive word is well and negative is miss whereas in social media dataset the most common positive word is like and negative is lost.
Conclusion
In conclusion, all four lexicons produce relatively similar sentiment patterns, despite differences in their scoring scales. In Pride and Prejudice, sentiment scores are mostly positive, which may reflect the nature of the novel’s storyline. In contrast, social media posts express a wider range of emotions, including both positive and negative sentiments. As a result, Instagram sentiment scores fluctuate more frequently, reflecting the varied emotions expressed in social media content.
Citation and Reference
Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach . O’Reilly Media. https://www.tidytextmining.com/
Anthropic. (2026). Claude (Claude Sonnet 5.5) [Large language model]. https://claude.ai/chat/911d9fd6-ccd1-4343-b8e7-dad5c7a96854
