R: sentiments analysis

library(tidytext)
sentiments

get_sentiments("afinn")
get_sentiments("bing")
get_sentiments("nrc")

library(janeaustenr)
library(dplyr)
library(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)

nrcjoy <- get_sentiments("nrc") %>%
    filter(sentiment == "joy")
tidy_books %>%
    filter(book == "Emma") %>%
    inner_join(nrcjoy) %>%
    count(word, sort = TRUE)

library(tidyr)
janeaustensentiment <- tidy_books %>%
    inner_join(get_sentiments("bing")) %>%
    count(book, index = linenumber %/% 80, sentiment) %>%
    spread(sentiment, n, fill = 0) %>%
    mutate(sentiment = positive - negative)

library(ggplot2)
ggplot(janeaustensentiment, aes(index, sentiment, fill = book)) +
    geom_col(show.legend = FALSE) +
    facet_wrap(~book, ncol = 2, scales = "free_x")

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• R