DataCamp

Divide & conquer: Using polarity for a comparison cloud

Now that you have seen how polarity can be used to divide a corpus, let's do it! This code will walk you through dividing a corpus based on sentiment so you can peer into the informaton in subsets instead of holistically.

Your R session has oz_pol which was created by applying polarity() to "The Wonderful Wizard of Oz."

For simplicity's sake, we created a simple custom function called pol_subsections() which will divide the corpus by polarity score. First, the function accepts a data frame with each row being a sentence or document of the corpus. The data frame is subset anywhere the polarity values are greater than or less than 0. Finally, the positive and negative sentences, non-zero polarities, are pasted with parameter collapse so that the terms are grouped into a single corpus. Lastly, the two documents are concatenated into a single vector of two distinct documents.

pol_subsections <- function(df) {
  x.pos <- subset(df$text, df$polarity > 0)
  x.neg <- subset(df$text, df$polarity < 0)
  x.pos <- paste(x.pos, collapse = " ")
  x.neg <- paste(x.neg, collapse = " ")
  all.terms <- c(x.pos, x.neg)
  return(all.terms)
}

At this point you have omitted the neutral sentences and want to focus on organizing the remaining text. In this exercise we use the %>% operator again to forward objects to functions. After some simple cleaning use comparison.cloud() to make the visual.

Within select() declare the first column text as text.var which is the raw text. The second column polarity should refer to the polarity scores polarity. Piped column names don't need $, you just use the column name without quotes.
Now apply pol_subsections() to oz_df. Call the new object all_terms.
To create all_corpus apply VectorSource() to all_terms and then %>% to VCorpus().
Create a TDM, all_tdm, using TermDocumentMatrix() on all_corpus.
- Add in the parameters control = list(removePunctuation = TRUE, stopwords = stopwords(kind = "en"))).
- Then %>% to as.matrix() and %>% again to set_colnames(c("positive", "negative")).
Apply comparison.cloud() to all_tdm with parameters max.words = 50, and colors = c("darkgreen","darkred").

Take Hint (-30 XP)

Exercise

Divide & conquer: Using polarity for a comparison cloud

Instructions