Exercise

Bing tidy polarity: Simple example

Now that you understand the basics of an inner join, let's apply this to the "Bing" lexicon. Keep in mind the inner_join() function comes from dplyr and the lexicon object is obtained using tidytext's get_sentiments() function'.

The Bing lexicon labels words as positive or negative. The next three exercises let you interact with this specific lexicon. To use get_sentiments() pass in a string such as "afinn", "bing", "nrc", or "loughran" to download the specific lexicon.

The inner join workflow:

  • Obtain the correct lexicon using get_sentiments().
  • Pass the lexicon and the tidy text data to inner_join().
  • In order for inner_join() to work there must be a shared column name. If there are no shared column names, declare them with an additional parameter, by equal to c with column names like below.
object <- x %>% 
    inner_join(y, by = c("column_from_x" = "column_from_y"))
  • Perform some aggregation and analysis on the table intersection.

Instructions

100 XP

We've loaded ag_txt containing the first 100 lines from Agamemnon and ag_tidy which is the tidy version.

  • For comparison, use polarity() on ag_txt.
  • Get the "bing" lexicon by passing that string to get_sentiments().
  • Perform an inner_join() with ag_tidy and bing.
    • The word columns are called "term" in ag_tidy & "word" in the lexicon, so declare the by argument.
    • Call the new object ag_bing_words.
  • Print ag_bing_words, and look at some of the words that are in the result.
  • Pass ag_bing_words to count() of sentiment using the pipe operator, %>%. Compare the polarity() score to sentiment count ratio.