Bing tidy polarity: Count & spread the white whale

In this exercise you will apply another inner_join() using the "bing" lexicon.

Then you will manipulate the results with both count() from dplyr and spread() from tidyr to learn about the text.

The spread() function spreads a key-value pair across multiple columns. In this case the key is the sentiment & the values are the frequency of positive or negative terms for each line. Using spread() changes the data so that each row now has positive and negative values, even if it is 0.

In this exercise, your R session has m_dick_tidy which contains the book Moby Dick and bing, containing the lexicon similar to the previous exercise.

Perform an inner_join() on m_dick_tidy and bing.
- As before, join the "term" column in m_dick_tidy to the "word" column in the lexicon.
- Call the new object moby_lex_words.
Create a column index, equal to as.numeric() applied to document. This occurs within mutate() in the tidyverse.
Create moby_count by forwarding moby_lex_words to count(), passing in sentiment, index.
Generate moby_spread by piping moby_count to spread() which contains sentiment, n, and fill = 0.

Exercise

Bing tidy polarity: Count & spread the white whale

Instructions