Sentiment dictionaries

Introduction to Text Analysis in R

Maham Faisal Khan

Senior Data Science Content Developer

Bing dictionary

get_sentiments("bing")

# A tibble: 6,788 x 2
   word        sentiment
   <chr>       <chr>    
 1 2-faced     negative 
 2 2-faces     negative 
 3 a+          positive 
 4 abnormal    negative 
 5 abolish     negative  
# … with 6,783 more rows

Bing dictionary

get_sentiments("bing") %>% 
  count(sentiment)

# A tibble: 2 x 2
  sentiment     n
  <chr>     <int>
1 negative   4782
2 positive   2006

Afinn dictionary

get_sentiments("afinn")

# A tibble: 2,476 x 2
   word       value
   <chr>      <int>
 1 abandon       -2
 2 abandoned     -2
 3 abandons      -2
 4 abducted      -2
 5 abduction     -2
# … with 2,471 more rows

Afinn dictionary

get_sentiments("afinn") %>% 
  summarize(
    min = min(value),
    max = max(value)
  )

# A tibble: 1 x 2
    min   max
  <dbl> <dbl>
1    -5     5

Loughran dictionary

sentiment_counts <- get_sentiments("loughran") %>% 
  count(sentiment) %>% 
  mutate(sentiment2 = fct_reorder(sentiment, n))

ggplot(sentiment_counts, aes(x = sentiment2, y = n)) +
  geom_col() +
  coord_flip() +
  labs(
    title = "Sentiment Counts in Loughran",
    x = "Counts",
    y = "Sentiment"
  )

Loughran dictionary

Let's practice!

Introduction to Text Analysis in R