Introduction

In this project we will be starting off by replicating the code from (Silge and Robinson 2021).
Once that is done we will b e repuding the results using a different sentiment analysis package called sentimentr, and instead of doing sentiment by word it will be done by chapter.

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)

books_by_chapter <- tidy_books %>%
  group_by(book, chapter) %>%
  summarise(text = paste(word, collapse = " ")) %>%
  ungroup()

## `summarise()` has grouped output by 'book'. You can override using the
## `.groups` argument.

nrc_joy <- get_sentiments("nrc") %>% 
  filter(sentiment == "joy")

tidy_books %>%
  filter(book == "Emma") %>%
  inner_join(nrc_joy) %>%
  count(word, sort = TRUE)

## Joining with `by = join_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
## # ℹ 291 more rows

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 with `by = join_by(word)`

## Warning in inner_join(., get_sentiments("bing")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 435434 of `x` matches multiple rows in `y`.
## ℹ Row 5051 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
##   "many-to-many"` to silence this warning.

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

The above code was from (Silge and Robinson 2021).
All code below was my own work for continuing the analysis.

sentiments <- sentimentr::sentiment_by(books_by_chapter$text)

## Warning: Each time `sentiment_by` is run it has to do sentence boundary disambiguation when a
## raw `character` vector is passed to `text.var`. This may be costly of time and
## memory.  It is highly recommended that the user first runs the raw `character`
## vector through the `get_sentences` function.

sentiments <- sentiments %>%
  mutate(sentiment = ifelse(ave_sentiment > 0, "Positive", "Negative"))

books_by_chapter$element_id <- 1:nrow(books_by_chapter)
sentiment_by_chapter <- merge(books_by_chapter, sentiments, by = "element_id")

ggplot(sentiment_by_chapter, aes(x=chapter, y=ave_sentiment, group=book, fill=book)) + 
  geom_col(show.legend = FALSE) +
  facet_wrap(~book, ncol = 2, scales = 'free_x') + 
  labs(x='Chapter', y='Average Sentiment')

Week 10

Brandon Cunningham

2024-03-27

Introduction

Conclusion