2.2 Sentiment analysis with inner join

library(janeaustenr)  

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)

tidy_books
#> # A tibble: 725,055 x 4
#>   book                linenumber chapter word       
#>   <fct>                    <int>   <int> <chr>      
#> 1 Sense & Sensibility          1       0 sense      
#> 2 Sense & Sensibility          1       0 and        
#> 3 Sense & Sensibility          1       0 sensibility
#> 4 Sense & Sensibility          3       0 by         
#> 5 Sense & Sensibility          3       0 jane       
#> 6 Sense & Sensibility          3       0 austen     
#> # ... with 7.25e+05 more rows

Because we name the count column word in unnest_tokens(), it’s convenient to join with the sentiment dataset:

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

tidy_books %>%
  filter(book == "Emma") %>%
  inner_join(nrc_joy) %>%
  count(word, sort = TRUE)
#> # A tibble: 303 x 2
#>   word       n
#>   <chr>  <int>
#> 1 good     359
#> 2 young    192
#> 3 friend   166
#> 4 hope     143
#> 5 happy    125
#> 6 love     117
#> # ... with 297 more rows

Next, we count up how many positive and negative words there are in defined sections of each book. We define an index here to keep track of where we are in the narrative; this index (using integer division) counts up sections of 80 lines of text.

jane_austen_sentiment <- tidy_books %>%
  inner_join(get_sentiments("bing")) %>%
  mutate(index = linenumber %/% 80) %>% 
  count(book, index, sentiment) %>%
  pivot_wider(names_from = sentiment, values_from = n, values_fill = list(n = 0)) %>%
  mutate(sentiment = positive - negative)

jane_austen_sentiment
#> # A tibble: 920 x 5
#>   book                index negative positive sentiment
#>   <fct>               <dbl>    <int>    <int>     <int>
#> 1 Sense & Sensibility     0       16       32        16
#> 2 Sense & Sensibility     1       19       53        34
#> 3 Sense & Sensibility     2       12       31        19
#> 4 Sense & Sensibility     3       15       31        16
#> 5 Sense & Sensibility     4       16       34        18
#> 6 Sense & Sensibility     5       16       51        35
#> # ... with 914 more rows

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