Sentiment Analysis
David Moste
3/31/2020
Introduction
For this assignment, I am tasked with getting an example from Text Mining with R running and then extending the example to a new corpus and a neew sentiment lexicon. Sections 1-6 are directly from Text Mining with R1. Section 7 is my extension.
1. Sentiments Dataset
library(tidytext)## Warning: package 'tidytext' was built under R version 3.6.3get_sentiments("afinn")## # A tibble: 2,477 x 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
## # ... with 2,467 more rowsget_sentiments("bing")## # A tibble: 6,786 x 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
## # ... with 6,776 more rowsget_sentiments("nrc")## # A tibble: 13,901 x 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
## # ... with 13,891 more rows2. Sentiment Analysis with Inner Join
library(janeaustenr)## Warning: package 'janeaustenr' was built under R version 3.6.3library(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, unionlibrary(stringr)
tidy_books <- 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)
nrc_joy <- get_sentiments("nrc") %>%
filter(sentiment == "joy")
tidy_books %>%
filter(book == "Emma") %>%
inner_join(nrc_joy) %>%
count(word, sort = TRUE)## Joining, by = "word"## # A tibble: 303 x 2
## word n
## <chr> <int>
## 1 good 359
## 2 young 192
## 3 friend 166
## 4 hope 143
## 5 happy 125
## 6 love 117
## 7 deal 92
## 8 found 92
## 9 present 89
## 10 kind 82
## # ... with 293 more rowslibrary(tidyr)
jane_austen_sentiment <- tidy_books %>%
inner_join(get_sentiments("bing")) %>%
count(book, index = linenumber %/% 80, sentiment) %>%
spread(sentiment, n, fill = 0) %>%
mutate(sentiment = positive - negative)## Joining, by = "word"library(ggplot2)
ggplot(jane_austen_sentiment, aes(index, sentiment, fill = book)) +
geom_col(show.legend = FALSE) +
facet_wrap(~book, ncol = 2, scales = "free_x")
3. Comparing the Three Dictionaries
pride_prejudice <- tidy_books %>%
filter(book == "Pride & Prejudice")
pride_prejudice## # A tibble: 122,204 x 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
## # ... with 122,194 more rowsafinn <- pride_prejudice %>%
inner_join(get_sentiments("afinn")) %>%
group_by(index = linenumber %/% 80) %>%
summarise(sentiment = sum(value)) %>%
mutate(method = "AFINN")## Joining, by = "word"bing_and_nrc <- bind_rows(pride_prejudice %>%
inner_join(get_sentiments("bing")) %>%
mutate(method = "Bing et al."),
pride_prejudice %>%
inner_join(get_sentiments("nrc") %>%
filter(sentiment %in% c("positive",
"negative"))) %>%
mutate(method = "NRC")) %>%
count(method, index = linenumber %/% 80, sentiment) %>%
spread(sentiment, n, fill = 0) %>%
mutate(sentiment = positive - negative)## Joining, by = "word"
## Joining, by = "word"bind_rows(afinn,
bing_and_nrc) %>%
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 x 2
## sentiment n
## <chr> <int>
## 1 negative 3324
## 2 positive 2312get_sentiments("bing") %>%
count(sentiment)## # A tibble: 2 x 2
## sentiment n
## <chr> <int>
## 1 negative 4781
## 2 positive 20054. Most Common Positive & Negative Words
bing_word_counts <- tidy_books %>%
inner_join(get_sentiments("bing")) %>%
count(word, sentiment, sort = TRUE) %>%
ungroup()## Joining, by = "word"bing_word_counts## # A tibble: 2,585 x 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
## # ... with 2,575 more rowsbing_word_counts %>%
group_by(sentiment) %>%
top_n(10) %>%
ungroup() %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = sentiment)) +
geom_col(show.legend = FALSE) +
facet_wrap(~sentiment, scales = "free_y") +
labs(y = "Contribution to sentiment",
x = NULL) +
coord_flip()## Selecting by n
custom_stop_words <- bind_rows(tibble(word = c("miss"),
lexicon = c("custom")),
stop_words)
custom_stop_words## # A tibble: 1,150 x 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
## # ... with 1,140 more rows5. Wordclouds
library(wordcloud)## Warning: package 'wordcloud' was built under R version 3.6.3## Loading required package: RColorBrewertidy_books %>%
anti_join(stop_words) %>%
count(word) %>%
with(wordcloud(word, n, max.words = 100))## Joining, by = "word"## Warning in wordcloud(word, n, max.words = 100): miss could not be fit on page.
## It will not be plotted.
library(reshape2)## Warning: package 'reshape2' was built under R version 3.6.3##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithstidy_books %>%
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, by = "word"
6. Looking Beyond Words
PandP_sentences <- tibble(text = prideprejudice) %>%
unnest_tokens(sentence, text, token = "sentences")
PandP_sentences$sentence[2]## [1] "however little known the feelings or views of such a man may be on his first entering a neighbourhood, this truth is so well fixed in the minds of the surrounding families, that he is considered the rightful property of some one or other of their daughters."austen_chapters <- austen_books() %>%
group_by(book) %>%
unnest_tokens(chapter, text, token = "regex",
pattern = "Chapter|CHAPTER [\\dIVXLC]") %>%
ungroup()
austen_chapters %>%
group_by(book) %>%
summarise(chapters = n())## # A tibble: 6 x 2
## book chapters
## <fct> <int>
## 1 Sense & Sensibility 51
## 2 Pride & Prejudice 62
## 3 Mansfield Park 49
## 4 Emma 56
## 5 Northanger Abbey 32
## 6 Persuasion 25bingnegative <- get_sentiments("bing") %>%
filter(sentiment == "negative")
wordcounts <- tidy_books %>%
group_by(book, chapter) %>%
summarize(words = n())
tidy_books %>%
semi_join(bingnegative) %>%
group_by(book, chapter) %>%
summarize(negativewords = n()) %>%
left_join(wordcounts, by = c("book", "chapter")) %>%
mutate(ratio = negativewords/words) %>%
filter(chapter != 0) %>%
top_n(1) %>%
ungroup()## Joining, by = "word"## Selecting by ratio## # A tibble: 6 x 5
## book chapter negativewords words ratio
## <fct> <int> <int> <int> <dbl>
## 1 Sense & Sensibility 43 161 3405 0.0473
## 2 Pride & Prejudice 34 111 2104 0.0528
## 3 Mansfield Park 46 173 3685 0.0469
## 4 Emma 15 151 3340 0.0452
## 5 Northanger Abbey 21 149 2982 0.0500
## 6 Persuasion 4 62 1807 0.03437. My Extension
For this section, I analyzed The Iliad using three different sentiment lexicons: SentiWordNet2, Hu Liu3, and SlangSD4.
# open libraries
library(lexicon)## Warning: package 'lexicon' was built under R version 3.6.3library(readr)
# open four lexicons: SenticNet, SentiWordNet, Hu Liu, and SlangSD
sentiword <- hash_sentiment_sentiword
names(sentiword) <- c("word","score")
huliu <- hash_sentiment_huliu
names(huliu) <- c("word","score")
slangsd <- hash_sentiment_slangsd
names(slangsd) <- c("word","score")
# open The Ilad text file and tidy it up with tidytext
iliad <- tibble(line = 1:length(read_lines("TheIliad.txt")),
text = c(read_lines("TheIliad.txt"))) %>%
unnest_tokens(word, text)
# create sentiments based on each of the lexicons
sentiword_sentiment <- iliad %>%
inner_join(sentiword, by = "word") %>%
group_by(index = line %/% 80) %>%
summarise(sentiment = sum(score)) %>%
mutate(overall = ifelse(sentiment > 0, "Positive", "Negative"))
huliu_sentiment <- iliad %>%
inner_join(huliu, by = "word") %>%
group_by(index = line %/% 80) %>%
summarise(sentiment = sum(score)) %>%
mutate(overall = ifelse(sentiment > 0, "Positive", "Negative"))
slangsd_sentiment <- iliad %>%
inner_join(slangsd, by = "word") %>%
group_by(index = line %/% 80) %>%
summarise(sentiment = sum(score)) %>%
mutate(overall = ifelse(sentiment > 0, "Positive", "Negative"))
# plot each lexicon sentiment
ggplot(sentiword_sentiment, aes(index, sentiment, fill = overall)) +
geom_col(show.legend = FALSE) + labs(title = "SentiWordNet")
ggplot(huliu_sentiment, aes(index, sentiment, fill = overall)) +
geom_col(show.legend = FALSE) + labs(title = "Hu Liu")
ggplot(slangsd_sentiment, aes(index, sentiment, fill = overall)) +
geom_col(show.legend = FALSE) + labs(title = "slangSD")
After looking at the sentiment of The Iliad using four different sentiment lexicons, I found it incredible how different the lexicons rated the same text. A text such as The Iliad should certainly come out as negative, but it was scired as neutral (Hu Liu and SentiWordNet) and negative (slangSD) depending on the sentiment lexicon used.
I decided to remove stop words from the sentiments to see what impact those might be having.
# remove stop words from each sentiment
sentiword_sentiment_go <- iliad %>%
inner_join(sentiword, by = "word") %>%
anti_join(stop_words, by = "word") %>%
group_by(index = line %/% 80) %>%
summarise(sentiment = sum(score)) %>%
mutate(overall = ifelse(sentiment > 0, "Positive", "Negative"))
huliu_sentiment_go <- iliad %>%
inner_join(huliu, by = "word") %>%
anti_join(stop_words, by = "word") %>%
group_by(index = line %/% 80) %>%
summarise(sentiment = sum(score)) %>%
mutate(overall = ifelse(sentiment > 0, "Positive", "Negative"))
slangsd_sentiment_go <- iliad %>%
inner_join(slangsd, by = "word") %>%
anti_join(stop_words, by = "word") %>%
group_by(index = line %/% 80) %>%
summarise(sentiment = sum(score)) %>%
mutate(overall = ifelse(sentiment > 0, "Positive", "Negative"))
# plot each lexicon sentiment
ggplot(sentiword_sentiment_go, aes(index, sentiment, fill = overall)) +
geom_col(show.legend = FALSE) + labs(title = "SentiWordNet - no stop words")
ggplot(huliu_sentiment_go, aes(index, sentiment, fill = overall)) +
geom_col(show.legend = FALSE) + labs(title = "Hu Liu - no stop words")
ggplot(slangsd_sentiment_go, aes(index, sentiment, fill = overall)) +
geom_col(show.legend = FALSE) + labs(title = "slangSD - no stop words")
All of these plots made sense for a story such as The Iliad, but I was curious now about which words were the largest contributors to the overall sentiment of the story.
iliad_words <- iliad %>%
count(word, sort = TRUE) %>%
anti_join(stop_words, by = "word") %>%
filter(n > 250)
ggplot(iliad_words, aes(x= reorder(word, n), y= n)) + geom_bar(stat = "identity", show.legend = FALSE) + coord_flip()
sentiword_words <- iliad %>%
inner_join(sentiword, by = "word") %>%
anti_join(stop_words, by = "word") %>%
group_by(word) %>%
summarise(sentiment = sum(score)) %>%
mutate(overall = ifelse(sentiment > 0, "Positive", "Negative")) %>%
filter(abs(sentiment) > 50)
huliu_words <- iliad %>%
inner_join(huliu, by = "word") %>%
group_by(word) %>%
summarise(sentiment = sum(score)) %>%
mutate(overall = ifelse(sentiment > 0, "Positive", "Negative")) %>%
filter(abs(sentiment) > 150)
slangsd_words <- iliad %>%
inner_join(slangsd, by = "word") %>%
anti_join(stop_words, by = "word") %>%
group_by(word) %>%
summarise(sentiment = sum(score)) %>%
mutate(overall = ifelse(sentiment > 0, "Positive", "Negative")) %>%
filter(abs(sentiment) > 50)
ggplot(sentiword_words, aes(x= reorder(word, -abs(sentiment)), y= sentiment, fill = overall)) + geom_bar(stat = "identity", show.legend = FALSE) + labs(title = "SentiWordNet", x = "Word", y = "Impact")
ggplot(huliu_words, aes(x= reorder(word, -abs(sentiment)), y= sentiment, fill = overall)) + geom_bar(stat = "identity", show.legend = FALSE) + labs(title = "Hu Liu", x = "Word", y = "Impact")
ggplot(slangsd_words, aes(x= reorder(word, -abs(sentiment)), y= sentiment, fill = overall)) + geom_bar(stat = "identity", show.legend = FALSE) + labs(title = "slangSD", x = "Word", y = "Impact")
mean(sentiword_sentiment_go$sentiment) - mean(sentiword_sentiment$sentiment)## [1] -2.144909mean(huliu_sentiment_go$sentiment) - mean(huliu_sentiment$sentiment)## [1] -4.305466mean(slangsd_sentiment_go$sentiment) - mean(slangsd_sentiment$sentiment)## [1] 6.852564Conclusion
After looking at the sentiment of The Iliad using three different sentiment lexicons, both with and without stop words, I find it incredible how different these lexicons are. Some lexicons (slangSD) swayed based on stop words, while others (SentiWordNet) had minimal change when stop words were removed.
After looking at the most impactful words from each sentiment lexicon, I believe the Hu Liu lexicon is the most approriate for this text. The most impactful words are meaningful even before stop words are removed and the overall sentiment outcome looks similar to what I would expect.
Citations
- Silge, Julia, and David Robinson. “Text Mining with R.” 2 Sentiment Analysis with Tidy Data, 7 Mar. 2020, www.tidytextmining.com/sentiment.html.
- Baccianella S., Esuli, A. and Sebastiani, F. (2010). SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. International Conference on Language Resources and Evaluation.
- Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-2004). Seattle, Washington. Hu, M., & Liu, B. (2004). Mining opinion features in customer reviews. National Conference on Artificial Intelligence.
- Wu, L., Morstatter, F., and Liu, H. (2016). SlangSD: Building and using a sentiment dictionary of slang words for short-text sentiment classification. CoRR. abs/1168.1058. 1-15.