sentiment2.R

library(janeaustenr)
library(stringr)
library(tidytext)
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)

positive_senti <- get_sentiments("bing") %>%
  filter(sentiment == "positive")
tidy_data %>%
  filter(book == "Emma") %>%
  semi_join(positive_senti) %>%
  count(word, sort = TRUE)

library(tidyr)
bing <- get_sentiments("bing")
Emma_sentiment <- tidy_data %>%
  inner_join(bing) %>%
  count(book = "Emma" , index = linenumber %/% 80, sentiment) %>%
  spread(sentiment, n, fill = 0) %>%
  mutate(sentiment = positive - negative)
         
library(ggplot2)
         ggplot(Emma_sentiment, aes(index, sentiment, fill = book)) +
           geom_bar(stat = "identity", show.legend = TRUE) +
           facet_wrap(~book, ncol = 2, scales = "free_x")
         
         counting_words <- tidy_data %>%
           inner_join(bing) %>%
           count(word, sentiment, sort = TRUE)
         head(counting_words)
         
         counting_words %>%
           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 = "Sentiment Score")
                
         library(reshape2)
         library(wordcloud)
         tidy_data %>%
           inner_join(bing) %>%
           count(word, sentiment, sort = TRUE) %>%
           acast(word ~ sentiment, value.var = "n", fill = 0) %>%
           comparison.cloud(colors = c("red", "dark green"),
                            max.words = 100)