Examine the sentiments data frame

So far you have used a single lexicon. Now we will transition to using three, each measuring sentiment in different ways.

The tidytext package contains a data frame called sentiments. The data frame contains over 23000 terms from three different subjectivity lexicons with corresponding information. Here are some example rows from the sentiments data frame.

Word	Sentiment	Lexicon	Score
abhorrent	NA	AFINN	-3
cool	NA	AFINN	1
congenial	positive	Bing	NA
enemy	negative	Bing	NA
ungrateful	anger	NRC	NA
sectarian	anger	NRC	NA

Notice the tidy format. Each word is a row and NAs fill in columns that are not applicable. The "AFINN" lexicon scores words from 5 to -5. The "Bing" lexicon is the same lexicon used in qdap's polarity() function. "Bing" words are only labeled as positive or negative. The "NRC" lexicon has distinct emotional classes covering Plutchik's Wheel and positive and negative.

Let's explore the sentiments data frame in more detail!

Use get_sentiments() to obtain the "afinn" lexicon, assigning to afinn_lex.
Review the overall count() of score in afinn_lex.

Take Hint (-9 XP)

Exercise

Examine the sentiments data frame

Instructions 1/3