Still, another useful preprocessing step involves word-stemming and stem completion. Word stemming reduces words to unify across documents. For example, the stem of "computational", "computers" and "computation" is "comput". But because "comput" isn't a real word, we want to reconstruct the words so that "computational", "computers", and "computation" all refer to a recognizable word, such as "computer". The reconstruction step is called stem completion.

The tm package provides the stemDocument() function to get to a word's root. This function either takes in a character vector and returns a character vector, or takes in a PlainTextDocument and returns a PlainTextDocument.

For example,

stemDocument(c("computational", "computers", "computation"))

returns "comput" "comput" "comput".

You will use stemCompletion() to reconstruct these word roots back into a known term. stemCompletion() accepts a character vector and a completion dictionary. The completion dictionary can be a character vector or a Corpus object. Either way, the completion dictionary for our example would need to contain the word "computer," so all instances of "comput" can be reconstructed.

Understanding text mining

Text Mining with Bag-of-Words in R

Практична інтерактивна вправа