2.4 Most common positive and negative words

bing_word_counts <- tidy_books %>%
  inner_join(get_sentiments("bing")) %>%
  count(word, sentiment, sort = TRUE) %>%
  ungroup()

bing_word_counts
#> # A tibble: 2,585 x 3
#>   word   sentiment     n
#>   <chr>  <chr>     <int>
#> 1 miss   negative   1855
#> 2 well   positive   1523
#> 3 good   positive   1380
#> 4 great  positive    981
#> 5 like   positive    725
#> 6 better positive    639
#> # ... with 2,579 more rows

The word “miss” is coded as negative but it is used as a title for young, unmarried women in Jane Austen’s works. If it were appropriate for our purposes, we could easily add “miss” to a custom stop-words list using bind_rows(). We could implement that with a strategy such as this：

custom_stop_words <- tibble(word = c("miss"), lexicon = c("custom")) %>% 
  bind_rows(stop_words)

bing_word_counts <- tidy_books %>%
  inner_join(get_sentiments("bing")) %>% 
  anti_join(custom_stop_words) %>%
  group_by(sentiment) %>%
  count(word, sentiment, sort = T) %>% 
  ungroup()

Then we can make a bar plot

bing_word_counts %>%
  group_by(sentiment) %>% 
  top_n(10) %>%
  ungroup()  %>% 
  facet_bar(y = word, x = n, by = sentiment, nrow = 1) + 
  labs(title = "Top 10 words of sentiment in Jane Austen's books")