Data607_Sentiment Analysis_Assignment
Mahmud Hasan Al Raji
2022-11-06
Sentiment analysis with tidy data
In this assignment I will perform sentiment analysis from different corpuses. As per the instruction, I will first recreate the sentiment analysis example code taken from chapter 2 of the book, “Text Mining with R” (Julia Silge & David Robinson) using the novel written by Jane Austen. Later, I will apply similar kind of sentiment analysis on H.G.Wells’s The Time Machine using a different sentiment lexicon.
Load packages
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(textdata)## Warning: package 'textdata' was built under R version 4.2.2library(tidytext)## Warning: package 'tidytext' was built under R version 4.2.2library(janeaustenr)## Warning: package 'janeaustenr' was built under R version 4.2.2library(dplyr)
library(stringr)
library(tidyr)
library(ggplot2)
library(wordcloud)## Warning: package 'wordcloud' was built under R version 4.2.2## Loading required package: RColorBrewerlibrary(reshape2)## Warning: package 'reshape2' was built under R version 4.2.2##
## Attaching package: 'reshape2'
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## The following object is masked from 'package:tidyr':
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## smithsGetting sentiment lexicons
Below are the codes for getting the three sentiment lexicons:
get_sentiments("afinn")## # 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
## # … with 2,467 more rowsget_sentiments("bing")## # 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
## # … with 6,776 more rowsget_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
## # … with 13,862 more rowsSentiment Analysis with Jane Austen Novels
Making the text is in a tidy format with one word per row.
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)Now, the text is in a tidy format with one word per row. So, we are ready to do the sentiment analysis. Finding the most joy words in Emma by performing the sentiment analysis by using the inner_join function.
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: 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
## # … with 291 more rowsWe see mostly positive, happy words about hope, friendship, and love here. We also see some words that may not be used joyfully by Austen (“found”, “present”).
Performing the sentiment analysis with bing lexicon by calculating a net sentiment (positive - negative).
jane_austen_sentiment <- 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, by = "word"Now we can plot these sentiment scores across the plot trajectory of
each novel. Notice that we are plotting against the index
on the x-axis that keeps track of narrative time in sections of
text.
ggplot(jane_austen_sentiment, aes(index, sentiment, fill = book)) +
geom_col(show.legend = FALSE) +
facet_wrap(~book, ncol = 2, scales = "free_x")
We can see from plot above, how the plot of each novel changes toward more positive or negative sentiment over the trajectory of the story.
Comparing the three sentiment dictionaries
Let’s use all three sentiment lexicons and examine how the sentiment
changes across the narrative arc of Pride and Prejudice. First,
let’s use filter() to choose only the words from the one
novel we are interested in.
pride_prejudice <- tidy_books %>%
filter(book == "Pride & Prejudice")
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
## # … with 122,194 more rowsCalculating the net sentiment with three lexicons:
afinn <- 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) %>%
pivot_wider(names_from = sentiment,
values_from = n,
values_fill = 0) %>%
mutate(sentiment = positive - negative)## Joining, by = "word"
## Joining, by = "word"Comparing the three sentiment lexicons using Pride and Prejudice
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")
The three different lexicons for calculating sentiment give results that are different in an absolute sense but have similar relative trajectories through the novel. We see similar dips and peaks in sentiment at about the same places in the novel, but the absolute values are significantly different. The AFINN lexicon gives the largest absolute values, with high positive values. The lexicon from Bing et al. has lower absolute values and seems to label larger blocks of contiguous positive or negative text. The NRC results are shifted higher relative to the other two, labeling the text more positively, but detects similar relative changes in the text. We find similar differences between the methods when looking at other novels; the NRC sentiment is high, the AFINN sentiment has more variance, the Bing et al. sentiment appears to find longer stretches of similar text, but all three agree roughly on the overall trends in the sentiment through a narrative arc.
Why is, for example, the result for the NRC lexicon biased so high in sentiment compared to the Bing et al. result? Let’s look briefly at how many positive and negative words are in these lexicons.
get_sentiments("nrc") %>%
filter(sentiment %in% c("positive", "negative")) %>%
count(sentiment)## # A tibble: 2 × 2
## sentiment n
## <chr> <int>
## 1 negative 3316
## 2 positive 2308get_sentiments("bing") %>%
count(sentiment)## # A tibble: 2 × 2
## sentiment n
## <chr> <int>
## 1 negative 4781
## 2 positive 2005Both lexicons have more negative than positive words, but the ratio of negative to positive words is higher in the Bing lexicon than the NRC lexicon. This will contribute to the effect we see in the plot above, as will any systematic difference in word matches, e.g. if the negative words in the NRC lexicon do not match the words that Jane Austen uses very well. Whatever the source of these differences, we see similar relative trajectories across the narrative arc, with similar changes in slope, but marked differences in absolute sentiment from lexicon to lexicon. This is all important context to keep in mind when choosing a sentiment lexicon for analysis.
Most common positive and negative words
# Finding each word's contribution to each sentiment
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 × 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 rows# Graphical representation of each word's contribution towards positive and negative sentiments
bing_word_counts %>%
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)
It is seen that the word ‘miss’ is contributing significantly towards
negative sentiment. But here the context of this word is not negative.
SO,we can create a custom stop word list to adjust for anomalies in the
sentiment lexicon used.
custom_stop_words <- bind_rows(tibble(word = c("miss"),
lexicon = c("custom")),
stop_words)
custom_stop_words## # 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
## # … with 1,140 more rowsWordclouds
# looking at the most common words as a whole with wordcloud.
tidy_books %>%
anti_join(stop_words) %>%
count(word) %>%
with(wordcloud(word, n, max.words = 100))## Joining, by = "word"
# Wordcloud of most frequent words splitted by postive or negative sentiment
tidy_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"
Looking at units beyond just words
# Example of tokenizing text into sentences
p_and_p_sentences <- tibble(text = prideprejudice) %>%
unnest_tokens(sentence, text, token = "sentences")
p_and_p_sentences$sentence[2]## [1] "by jane austen"Splitting the text of Jane Austen’s novels into a data frame by chapter by using regex pattern.
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 × 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 25Finding the chapter that has the highest proportion of negative words in each book
bingnegative <- get_sentiments("bing") %>%
filter(sentiment == "negative")
wordcounts <- tidy_books %>%
group_by(book, chapter) %>%
summarize(words = n())## `summarise()` has grouped output by 'book'. You can override using the
## `.groups` argument.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) %>%
slice_max(ratio, n = 1) %>%
ungroup()## Joining, by = "word"
## `summarise()` has grouped output by 'book'. You can override using the
## `.groups` argument.## # A tibble: 6 × 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 0.0343New corpus and lexicon
Previously i used the janeaustenr package to explore tidying text. Now, i am going to use the gutenbergr package (Robinson 2016). The gutenbergr package provides access to the public domain works from the Project Gutenberg collection. The package includes tools both for downloading books and a complete dataset of Project Gutenberg metadata that can be used to find works of interest. Here, I am going to perform sentiment analysis for different lexicons on ‘The Time Machine’ which a novel written by H.G.Wells.
Installation and loading gutenbergr package
devtools::install_github("ropensci/gutenbergr")## Skipping install of 'gutenbergr' from a github remote, the SHA1 (9d245385) has not changed since last install.
## Use `force = TRUE` to force installationlibrary(gutenbergr)Sentiment Analysis for H.G.Wells’s ‘The Time Machine’ novel.
#Load the book, tidying the text into word token
the_time_machine<- gutenberg_download(35)## Determining mirror for Project Gutenberg from http://www.gutenberg.org/robot/harvest## Using mirror http://aleph.gutenberg.orgtidy_time_machine <- the_time_machine %>%
unnest_tokens(word, text) %>%
anti_join(stop_words)## Joining, by = "word"# Most common words in the book
tidy_time_machine %>%
count(word, sort = TRUE)## # A tibble: 4,172 × 2
## word n
## <chr> <int>
## 1 time 207
## 2 machine 88
## 3 white 61
## 4 traveller 57
## 5 hand 49
## 6 morlocks 48
## 7 people 46
## 8 weena 46
## 9 found 44
## 10 light 43
## # … with 4,162 more rows# Wordcloud of most frequent words
tidy_time_machine %>%
count(word) %>%
with(wordcloud(word, n, max.words =100))
Use of new lexicon “louhgran”
get_sentiments("loughran") %>%
filter(sentiment %in% c("positive", "negative")) %>%
count(sentiment)## # A tibble: 2 × 2
## sentiment n
## <chr> <int>
## 1 negative 2355
## 2 positive 354sentiment_positive <- get_sentiments("loughran") %>%
filter(sentiment == "positive")
tidy_time_machine%>%
inner_join(sentiment_positive) %>%
count(word, sort=TRUE) %>%
ungroup()## Joining, by = "word"## # A tibble: 69 × 2
## word n
## <chr> <int>
## 1 beautiful 12
## 2 strong 10
## 3 perfect 9
## 4 strength 9
## 5 abundant 8
## 6 assured 8
## 7 dream 7
## 8 pleasant 7
## 9 incredible 6
## 10 perfectly 5
## # … with 59 more rows# Wordcloud of most frequent words split by loughran sentiment
tidy_time_machine %>%
inner_join(get_sentiments("loughran")) %>%
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"
Finding the contribution of words to different sentiments with loughran lexicon
loughran_word_counts <- tidy_time_machine %>%
inner_join(get_sentiments("loughran")) %>%
count(word, sentiment, sort = TRUE) %>%
ungroup()## Joining, by = "word"loughran_word_counts## # A tibble: 349 × 3
## word sentiment n
## <chr> <chr> <int>
## 1 fear negative 24
## 2 suddenly uncertainty 22
## 3 doubt negative 13
## 4 doubt uncertainty 13
## 5 sudden uncertainty 13
## 6 beautiful positive 12
## 7 appeared uncertainty 11
## 8 possibly uncertainty 11
## 9 slower negative 11
## 10 broken negative 10
## # … with 339 more rowsloughran_word_counts %>%
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)
The section below repeats the analysis with the bing lexicon to categorize words as positive and negative
bing_word_counts <- tidy_time_machine %>%
inner_join(get_sentiments("bing")) %>%
count(word, sentiment, sort = TRUE) %>%
ungroup()## Joining, by = "word"bing_word_counts## # A tibble: 816 × 3
## word sentiment n
## <chr> <chr> <int>
## 1 strange negative 32
## 2 darkness negative 29
## 3 dark negative 27
## 4 fear negative 24
## 5 fell negative 16
## 6 soft positive 16
## 7 bright positive 15
## 8 struck negative 15
## 9 odd negative 14
## 10 dim negative 13
## # … with 806 more rowsbing_word_counts %>%
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)
Finding joy words using nrc lexicon
nrc_joy <- get_sentiments("nrc") %>%
filter(sentiment == "joy")
tidy_time_machine %>%
inner_join(nrc_joy) %>%
count(word, sort = TRUE)## Joining, by = "word"## # A tibble: 164 × 2
## word n
## <chr> <int>
## 1 found 44
## 2 sun 29
## 3 green 28
## 4 feeling 20
## 5 beautiful 12
## 6 save 11
## 7 comfort 10
## 8 humanity 10
## 9 perfect 9
## 10 abundant 8
## # … with 154 more rowsReferences
Robinson, J. S. and D. (n.d.). Sentiment analysis with Tidy Data Text mining with R. https://www.tidytextmining.com/sentiment.html
The Time Machine by H.G.Wells. https://www.gutenberg.org/ebooks/35