## ----setup, echo=FALSE--------------------------------------------------------
library(knitr)
opts_chunk$set(warning = FALSE, message = FALSE,
eval = requireNamespace("wordcloud", quietly = TRUE))
library(ggplot2)
theme_set(theme_light())
## -----------------------------------------------------------------------------
library(janeaustenr)
library(dplyr)
library(stringr)
original_books <- austen_books() %>%
group_by(book) %>%
mutate(line = row_number(),
chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]",
ignore_case = TRUE)))) %>%
ungroup()
original_books
## -----------------------------------------------------------------------------
library(tidytext)
tidy_books <- original_books %>%
unnest_tokens(word, text)
tidy_books
## -----------------------------------------------------------------------------
cleaned_books <- tidy_books %>%
anti_join(get_stopwords())
## -----------------------------------------------------------------------------
cleaned_books %>%
count(word, sort = TRUE)
## -----------------------------------------------------------------------------
positive <- get_sentiments("bing") %>%
filter(sentiment == "positive")
tidy_books %>%
filter(book == "Emma") %>%
semi_join(positive) %>%
count(word, sort = TRUE)
## -----------------------------------------------------------------------------
library(tidyr)
bing <- get_sentiments("bing")
janeaustensentiment <- tidy_books %>%
inner_join(bing) %>%
count(book, index = line %/% 80, sentiment) %>%
spread(sentiment, n, fill = 0) %>%
mutate(sentiment = positive - negative)
## ---- fig.width=7, fig.height=7, warning=FALSE--------------------------------
library(ggplot2)
ggplot(janeaustensentiment, aes(index, sentiment, fill = book)) +
geom_bar(stat = "identity", show.legend = FALSE) +
facet_wrap(~book, ncol = 2, scales = "free_x")
## -----------------------------------------------------------------------------
bing_word_counts <- tidy_books %>%
inner_join(bing) %>%
count(word, sentiment, sort = TRUE)
bing_word_counts
## ---- fig.width=7, fig.height=6-----------------------------------------------
bing_word_counts %>%
filter(n > 150) %>%
mutate(n = ifelse(sentiment == "negative", -n, n)) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = sentiment)) +
geom_col() +
coord_flip() +
labs(y = "Contribution to sentiment")
## ---- fig.height=6, fig.width=6-----------------------------------------------
library(wordcloud)
cleaned_books %>%
count(word) %>%
with(wordcloud(word, n, max.words = 100))
## ----wordcloud, fig.height=5, fig.width=5-------------------------------------
library(reshape2)
tidy_books %>%
inner_join(bing) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(colors = c("#F8766D", "#00BFC4"),
max.words = 100)
## -----------------------------------------------------------------------------
PandP_sentences <- tibble(text = prideprejudice) %>%
unnest_tokens(sentence, text, token = "sentences")
## -----------------------------------------------------------------------------
PandP_sentences$sentence[2]
## -----------------------------------------------------------------------------
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())
## -----------------------------------------------------------------------------
bingnegative <- 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)
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