Emotion Analysis - v1
Partha Majumdar
09/11/2019
Emotion Analysis
This document contains the complete code for Emotion Analysis of Jane Austen’s book Emma.
if("tidytext" %in% rownames(installed.packages()) == FALSE)
{install.packages("tidytext", dependencies=TRUE)}
library(tidytext)
if("textdata" %in% rownames(installed.packages()) == FALSE)
{install.packages("textdata", dependencies=TRUE)}
library(textdata)
if("janeaustenr" %in% rownames(installed.packages()) == FALSE)
{install.packages("janeaustenr", dependencies=TRUE)}
library(janeaustenr)
if("stringr" %in% rownames(installed.packages()) == FALSE)
{install.packages("stringr", dependencies=TRUE)}
library(stringr)
if("dplyr" %in% rownames(installed.packages()) == FALSE)
{install.packages("dplyr", dependencies=TRUE)}
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, unionif("tidyr" %in% rownames(installed.packages()) == FALSE)
{install.packages("tidyr", dependencies=TRUE)}
library(tidyr)
if("ggplot2" %in% rownames(installed.packages()) == FALSE)
{install.packages("ggplot2", dependencies=TRUE)}
library(ggplot2)
if("ggthemes" %in% rownames(installed.packages()) == FALSE)
{install.packages("ggthemes", dependencies=TRUE)}
library(ggthemes)
if("reshape2" %in% rownames(installed.packages()) == FALSE)
{install.packages("reshape2", dependencies=TRUE)}
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithsif("wordcloud" %in% rownames(installed.packages()) == FALSE)
{install.packages("wordcloud", dependencies=TRUE)}
library(wordcloud)## Loading required package: RColorBrewerif("igraph" %in% rownames(installed.packages()) == FALSE)
{install.packages("igraph")}
library(igraph)##
## Attaching package: 'igraph'## The following object is masked from 'package:tidyr':
##
## crossing## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## unionif("ggraph" %in% rownames(installed.packages()) == FALSE)
{install.packages("ggraph")}
library(ggraph)
tidy_data <- 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) %>%
anti_join(stop_words)## Joining, by = "word"tidy_data## # A tibble: 217,609 x 4
## book linenumber chapter word
## <fct> <int> <int> <chr>
## 1 Sense & Sensibility 1 0 sense
## 2 Sense & Sensibility 1 0 sensibility
## 3 Sense & Sensibility 3 0 jane
## 4 Sense & Sensibility 3 0 austen
## 5 Sense & Sensibility 5 0 1811
## 6 Sense & Sensibility 10 1 chapter
## 7 Sense & Sensibility 10 1 1
## 8 Sense & Sensibility 13 1 family
## 9 Sense & Sensibility 13 1 dashwood
## 10 Sense & Sensibility 13 1 settled
## # … with 217,599 more rowsv_book <- "Emma"
v_lexicon <- "nrc"
positive_emotions <- c("positive","joy","anticipation","surprise", "trust")
negative_emotions <- c("negative","anger","disgust","fear", "sadness")
positive_sentiment <- get_sentiments(v_lexicon) %>%
filter(sentiment %in% positive_emotions)
#################################
# MOST COMMON WORDS IN THE BOOK
#################################
tidy_data %>%
filter(book == v_book) %>%
semi_join(positive_sentiment) %>%
count(word, sort = TRUE)## Joining, by = "word"## # A tibble: 1,114 x 2
## word n
## <chr> <int>
## 1 time 279
## 2 dear 241
## 3 frank 200
## 4 father 168
## 5 friend 166
## 6 hope 143
## 7 happy 125
## 8 love 117
## 9 letter 109
## 10 doubt 98
## # … with 1,104 more rowstidy_data %>%
filter(book == v_book) %>%
count(word, sort = TRUE) %>%
filter(n > 70) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n)) +
geom_col() +
xlab(NULL) +
coord_flip() + ggtitle(paste("The Most Common Words in the Novel - ", v_book))
#################################
# WORD CONTRIBUTION TO SENTIMENTS
#################################
book_sentiment <- tidy_data %>%
inner_join(get_sentiments(v_lexicon)) %>%
count(book = v_book, word,
index = linenumber %/% 80, sentiment) %>%
mutate(original_n = n) %>%
mutate(positive_negative = ifelse(sentiment %in% c('positive','joy','surprise','trust','anticipation'), 'positive', 'negative')) %>%
mutate(original_sentiment = sentiment) %>%
spread(sentiment, n, fill = 0) %>%
mutate(sentiment = (positive+joy+surprise+trust+anticipation) - (negative+anger+disgust+sadness+fear)) %>%
ungroup()## Joining, by = "word"book_sentiment## # A tibble: 110,044 x 17
## book word index original_n positive_negati… original_sentim… anger
## <chr> <chr> <dbl> <int> <chr> <chr> <dbl>
## 1 Emma aban… 63 1 negative anger 1
## 2 Emma aban… 63 1 negative fear 0
## 3 Emma aban… 63 1 negative negative 0
## 4 Emma aban… 63 1 negative sadness 0
## 5 Emma abhor 17 1 negative anger 1
## 6 Emma abhor 17 1 negative disgust 0
## 7 Emma abhor 17 1 negative fear 0
## 8 Emma abhor 17 1 negative negative 0
## 9 Emma abhor 51 1 negative anger 1
## 10 Emma abhor 51 1 negative disgust 0
## # … with 110,034 more rows, and 10 more variables: anticipation <dbl>,
## # disgust <dbl>, fear <dbl>, joy <dbl>, negative <dbl>, positive <dbl>,
## # sadness <dbl>, surprise <dbl>, trust <dbl>, sentiment <dbl>book_sentiment %>%
filter(original_n > 5) %>%
group_by(original_sentiment) %>%
top_n(5) %>%
ungroup() %>%
mutate(word = reorder(word, original_n)) %>%
ggplot(aes(word, original_n, fill = original_sentiment)) +
geom_col(show.legend = FALSE) +
facet_wrap(~original_sentiment, scales = "free_y") +
labs(y = "Words Contribution to Individual Sentiments",
x = NULL) +
coord_flip()## Selecting by sentiment
################################
# POSITIVE & NEGATIVE SENTIMENTS
################################
book_nrc <- book_sentiment %>%
mutate(Pos_Neg = ifelse(original_sentiment %in% c("positive","joy","anticipation","trust","surprise"), "positive", "negative")) %>%
mutate(original_n = ifelse(Pos_Neg == "negative", -original_n, original_n))
ggplot(data = book_nrc, aes(x = index, y = original_n, fill = Pos_Neg)) +
geom_bar(stat = 'identity', position = position_dodge()) +
theme_minimal() +
ylab("Sentiment") +
ggtitle(paste("Positive and Negative Sentiment in Novel - ", v_book)) +
scale_color_manual(values = c("red", "dark green")) +
scale_fill_manual(values = c("red", "dark green"))
####################
# SENTIMENTS
####################
emotions <- book_nrc %>%
select(index, anger, anticipation, disgust, fear, joy,
sadness, surprise, trust) %>%
melt(id = "index")
names(emotions) <- c("linenumber", "sentiment", "value")
emotions_group <- group_by(emotions, sentiment)
by_emotions <- summarise(emotions_group, values=sum(value))
ggplot(aes(reorder(x=sentiment, values), y=values, fill=sentiment), data = by_emotions) +
geom_bar(stat = 'identity') +
ggtitle(paste('Sentiment in Novel - ', v_book)) +
coord_flip() +
theme(legend.position="none")
####################
# SENTIMENT SCORE
####################
word_count <- tidy_data %>%
filter(book == v_book) %>%
inner_join(get_sentiments(v_lexicon)) %>%
count(word, sentiment, sort = TRUE)## Joining, by = "word"head(word_count)## # A tibble: 6 x 3
## word sentiment n
## <chr> <chr> <int>
## 1 time anticipation 279
## 2 dear positive 241
## 3 frank positive 200
## 4 frank trust 200
## 5 father trust 168
## 6 friend joy 166word_count %>%
filter(n > 25) %>%
mutate(n = ifelse(sentiment %in% negative_emotions, -n, n)) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = sentiment))+
geom_col() +
coord_flip() +
labs(y = "Sentiment Score")
####################
# WORD CLOUD
####################
tidy_data %>%
filter(book == v_book) %>%
inner_join(get_sentiments(v_lexicon)) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(
colors = c("red","dark green","blue","grey","magenta","brown","orange","mediumaquamarine","navy"),
max.words = 500)## Joining, by = "word"## Warning in comparison.cloud(., colors = c("red", "dark green", "blue",
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####################
# ANALYSING BIGRAMS
####################
book_bigrams <- austen_books() %>%
group_by(book) %>%
filter(book == v_book) %>%
unnest_tokens(bigram, text, token = "ngrams", n = 2)
book_bigrams## # A tibble: 160,995 x 2
## # Groups: book [1]
## book bigram
## <fct> <chr>
## 1 Emma emma by
## 2 Emma by jane
## 3 Emma jane austen
## 4 Emma austen volume
## 5 Emma volume i
## 6 Emma i chapter
## 7 Emma chapter i
## 8 Emma i emma
## 9 Emma emma woodhouse
## 10 Emma woodhouse handsome
## # … with 160,985 more rowsbigrams_separated <- book_bigrams %>%
separate(bigram, c("word1", "word2"), sep = " ")
bigrams_filtered <- bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
bigram_counts <- bigrams_filtered %>%
count(word1, word2, sort = TRUE)
bigram_counts## # A tibble: 7,487 x 4
## # Groups: book [1]
## book word1 word2 n
## <fct> <chr> <chr> <int>
## 1 Emma miss woodhouse 162
## 2 Emma frank churchill 132
## 3 Emma miss fairfax 109
## 4 Emma miss bates 103
## 5 Emma jane fairfax 96
## 6 Emma john knightley 56
## 7 Emma miss smith 51
## 8 Emma miss taylor 40
## 9 Emma dear emma 31
## 10 Emma maple grove 31
## # … with 7,477 more rowsbigrams_united <- bigrams_filtered %>%
unite(bigram, word1, word2, sep = " ")
bigrams_united## # A tibble: 9,524 x 2
## # Groups: book [6]
## book bigram
## <fct> <chr>
## 1 Emma jane austen
## 2 Emma austen volume
## 3 Emma emma woodhouse
## 4 Emma woodhouse handsome
## 5 Emma handsome clever
## 6 Emma comfortable home
## 7 Emma happy disposition
## 8 Emma affectionate indulgent
## 9 Emma indulgent father
## 10 Emma sister's marriage
## # … with 9,514 more rowsbigram_tf_idf <- bigrams_united %>%
count(bigram)
bigram_tf_idf <- bigram_tf_idf %>%
filter(n > 10)
ggplot(aes(x = reorder(bigram, n), y=n), data=bigram_tf_idf) +
geom_bar(stat = 'identity') +
ggtitle(paste("The Most Common Bigrams in Novel -", v_book)) +
coord_flip()
bigram_graph_dataframe <- bigram_counts[ , !(names(bigram_counts) %in% c("book"))]
bigram_graph <- bigram_graph_dataframe %>%
filter(n > 5) %>%
graph_from_data_frame()
bigram_graph## IGRAPH cc96487 DN-- 68 55 --
## + attr: name (v/c), n (e/n)
## + edges from cc96487 (vertex names):
## [1] miss ->woodhouse frank ->churchill miss ->fairfax
## [4] miss ->bates jane ->fairfax john ->knightley
## [7] miss ->smith miss ->taylor dear ->emma
## [10] maple ->grove cried ->emma harriet->smith
## [13] robert ->martin dear ->miss colonel->campbell
## [16] frank ->churchill's box ->hill miss ->fairfax's
## [19] miss ->hawkins replied->emma dear ->jane
## [22] jane ->fairfax's poor ->miss poor ->harriet
## + ... omitted several edgesset.seed(2019)
ggraph(bigram_graph, layout = "kk") +
geom_edge_link() +
geom_node_point(color = "darkslategray4", size = 3) +
geom_node_text(aes(label = name), vjust = 1.8) +
ggtitle(paste("Most Common Bigrams in the Novel - ",v_book))
####################
# ANALYSING TRIGRAMS
####################
book_trigrams <- austen_books() %>%
group_by(book) %>%
filter(book == v_book) %>%
unnest_tokens(trigram, text, token = "ngrams", n = 3)
book_trigrams## # A tibble: 160,994 x 2
## # Groups: book [1]
## book trigram
## <fct> <chr>
## 1 Emma emma by jane
## 2 Emma by jane austen
## 3 Emma jane austen volume
## 4 Emma austen volume i
## 5 Emma volume i chapter
## 6 Emma i chapter i
## 7 Emma chapter i emma
## 8 Emma i emma woodhouse
## 9 Emma emma woodhouse handsome
## 10 Emma woodhouse handsome clever
## # … with 160,984 more rowstrigrams_separated <- book_trigrams %>%
separate(trigram, c("word1", "word2", "word3"), sep = " ")
trigrams_filtered <- trigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(!word3 %in% stop_words$word)
trigram_counts <- trigrams_filtered %>%
count(word1, word2, word3, sort = TRUE)
trigram_counts## # A tibble: 1,802 x 5
## # Groups: book [1]
## book word1 word2 word3 n
## <fct> <chr> <chr> <chr> <int>
## 1 Emma dear miss woodhouse 23
## 2 Emma poor miss taylor 11
## 3 Emma abbey mill farm 5
## 4 Emma marrying jane fairfax 4
## 5 Emma woman lovely woman 4
## 6 Emma bad sore throat 3
## 7 Emma box hill party 3
## 8 Emma harriet cried emma 3
## 9 Emma lovely woman reigns 3
## 10 Emma miss fairfax's situation 3
## # … with 1,792 more rowstrigrams_united <- trigrams_filtered %>%
unite(trigram, word1, word2, word3, sep = " ")
trigrams_united## # A tibble: 1,893 x 2
## # Groups: book [6]
## book trigram
## <fct> <chr>
## 1 Emma jane austen volume
## 2 Emma emma woodhouse handsome
## 3 Emma woodhouse handsome clever
## 4 Emma affectionate indulgent father
## 5 Emma highly esteeming miss
## 6 Emma esteeming miss taylor's
## 7 Emma miss taylor's judgment
## 8 Emma disagreeable consciousness miss
## 9 Emma consciousness miss taylor
## 10 Emma miss taylor married
## # … with 1,883 more rowstrigram_tf_idf <- trigrams_united %>%
count(trigram)
trigram_tf_idf <- trigram_tf_idf %>%
filter(n > 1)
ggplot(aes(x = reorder(trigram, n), y=n), data=trigram_tf_idf) +
geom_bar(stat = 'identity') +
ggtitle(paste("The Most Common Trigrams in Novel -", v_book)) +
coord_flip()