Changing frequency weights
So far, you've simply counted terms in documents in the DocumentTermMatrix or TermDocumentMatrix. In this exercise, you'll learn about TfIdf weighting instead of simple term frequency. TfIdf stands for term frequency-inverse document frequency and is used when you have a large corpus with limited-term diversity.
TfIdf counts terms (i.e. Tf), normalizes the value by document length and then penalizes the value the more often a word appears among the documents. This is common sense; if a word is commonplace, it's important but not insightful. This penalty aspect is captured in the inverse document frequency (i.e., Idf).
For example, reviewing customer service notes may include the term "cu" as shorthand for "customer". One note may state "the cu has a damaged package" and another as "cu called with question about delivery". With document frequency weighting, "cu" appears twice, so it is expected to be informative. However, in TfIdf, "cu" is penalized because it appears in all the documents. As a result, "cu" isn't considered novel, so its value is reduced towards 0, which lets other terms have higher values for analysis.
This exercise is part of the course
Text Mining with Bag-of-Words in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create a TDM
tdm <- ___
# Convert it to a matrix
tdm_m <- ___
# Examine part of the matrix
tdm_m[___, ___]