2.5 Comparing the three sentiment dictionaries

• Lexicons have diff quality

• Use three sentiment lexicons and examine how the sentiment changes across the narrative arc of Pride and Prejudice.

pride_prejudice <- tidy_books %>% 
  filter(book == "Pride & Prejudice")
pride_prejudice

## # A tibble: 122,204 × 4
##    book              linenumber chapter word     
##    <fct>                  <int>   <int> <chr>    
##  1 Pride & Prejudice          1       0 pride    
##  2 Pride & Prejudice          1       0 and      
##  3 Pride & Prejudice          1       0 prejudice
##  4 Pride & Prejudice          3       0 by       
##  5 Pride & Prejudice          3       0 jane     
##  6 Pride & Prejudice          3       0 austen   
##  7 Pride & Prejudice          7       1 chapter  
##  8 Pride & Prejudice          7       1 1        
##  9 Pride & Prejudice         10       1 it       
## 10 Pride & Prejudice         10       1 is       
## # … with 122,194 more rows

afinn <- pride_prejudice %>% 
  inner_join(get_sentiments("afinn")) %>% 
  group_by(index = linenumber %/% 80) %>% 
  summarise(sentiment = sum(value)) %>% 
  mutate(method = "AFINN")

bin_pride_prejudice <-  pride_prejudice %>% 
    inner_join(get_sentiments("bing")) %>%
    mutate(method = "Bing et al.") %>% 
 count(method, index = linenumber %/% 80, sentiment) %>%
  pivot_wider(names_from = sentiment,
              values_from = n,
              values_fill = 0) %>% 
  mutate(sentiment = positive - negative)

## Joining, by = "word"

bind_rows(afinn, 
          bin_pride_prejudice) %>%
  ggplot(aes(index, sentiment, fill = method)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~method, ncol = 1, scales = "free_y")

• The lexicons for calculating sentiment give results that are different in an absolute sense but have similar relative trajectories through the nove