DataCamp

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," grepl(), function 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.

This exercise introduces rownames(). This function declares the names of rows in a data frame. It behaves a bit differently because rownames() is passed the object gaining the row names on the left side of <-. On the right side the character vector of names is declared such as data_frame[, 1]. For instance:

rownames(data_frame) <- vector_of_names

After setting row names you will create a more varied comparison.cloud().

inner_join() moby and nrc to create moby_sentiment.
Using filter() with a negation (!) and grepl() search for "positive|negative". The column to search is called sentiment.
Forward moby_pos_neg to count()with sentiment then term = term. %>% this to spread() passing in sentiment, n, and fill = 0 and then change it to a data frame with as.data.frame().
Set the rownames() of moby_tidy to be the first column vector, moby_tidy[, 1].
Drop the first column of moby_tidy (since it was declared as row names) by setting moby_tidy[, 1] to NULL.
Examine moby_tidy using head().
Apply comparison.cloud() to moby_tidy with max.words = 50, title.size = 1.5.

Take Hint (-30 XP)

Exercise

Emotional introspection

Instructions