Emotional introspection
In this exercise you go beyond subsetting on positive and negative language. Instead you will subset text by each of the 8 emotions in Plutchik's emotional wheel to construct a visual. With this approach you will get more clarity in word usage by mapping to a specific emotion instead of just positive or negative.
Using the tidytext subjectivity lexicon, "nrc", you perform an inner_join() with your text. The "nrc" lexicon has the 8 emotions plus positive and negative term classes. So you will have to drop positive and negative words after performing your inner_join(). One way to do so is with the negation, !, and grepl().
The "Global Regular Expression Print Logical" function, grepl(), will return a True or False if a string pattern is identified in each row. In this exercise you will search for positive OR negative using the | operator, representing "or" as shown below. Often this straight line is above the enter key on a keyboard. Since the ! negation precedes grepl(), the T or F is switched so the "positive|negative" is dropped instead of kept.
Object <- tibble %>%
filter(!grepl("positive|negative", column_name))
Next you apply count() on the identified words along with pivot_wider() to get the data frame organized.
comparison.cloud() requires its input to have row names, so you'll have to convert it to a base-R data.frame, calling data.frame() with the row.names argument.
Cet exercice fait partie du cours
Sentiment Analysis in R
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
moby_tidy <- moby %>%
# Inner join to nrc lexicon
___(___, by = c("term" = "word")) %>%
# Drop positive or negative
___(!___("___", sentiment)) %>%
# Count by sentiment and term
___(___, ___) %>%
# Pivot sentiment, using n for values
___(names_from = ___, values_from = ___, values_fill = ___) %>%
# Convert to data.frame, making term the row names
data.frame(row.names = "___")
# Examine
head(moby_tidy)