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Exercise

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 spread() 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.

Instructions 1/2
XP
  • 1
  • 2
  • inner_join() moby to nrc.
  • Using filter() with a negation (!) and grepl() search for "positive|negative". The column to search is called sentiment.
  • Use count() to count by sentiment and term.
  • Reshape the data frame with spread(), passing in sentiment, n, and fill = 0.
  • Convert to plain data frame with data.frame(), making the term column into rownames.
  • Examine moby_tidy using head().