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.

This exercise is part of the course

Sentiment Analysis in R

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Exercise instructions

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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Qdap polarity
___

# Get Bing lexicon
bing <- get_sentiments("___")

# Join text to lexicon
ag_bing_words <- ___(___, ___, by = c("___" = "___"))

# Examine
ag_bing_words

# Get counts by sentiment
ag_bing_words %>%
  ___(___)

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