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Performing Natural Language Processing with R

Stopword Removal

Explore stopword removal techniques using the tm package in R to clean text data for structured analysis. Understand how to apply built-in and custom stopword dictionaries, compare results, and improve natural language processing workflows by focusing on meaningful words.

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Stopwords

In our project, we use words from each novel to identify interesting discussion groups. Words like “and,” “the,” or “that” are too common to have any use for this task. What we need is to remove these types of words from consideration.

Introduction to stopword removal

Stopwords make sentences pleasant to read and sometimes clarify the context of associated words. For the most part, they aren’t important for natural language processing. An important part of text mining is removing these connecting words, which is called stopword removal.

Let’s look at a simple example:

R
library(tm, quietly=TRUE)
myText <- "Stopwords are nice words for humans. 
They make sentences pleasant to read and sometimes clarify 
the context of associated words, but for the most part, 
they aren't important for natural language processing. 
An important part of text mining is removing these 
extraneous words; it's called stopword removal."
removeWords(myText, stopwords("english"))

  • Line 3: The myText <- "Stopwords are nice words for humans. ... it's called stopword removal." line assigns a multi-sentence text to the variable myText. The text contains several sentences.

  • Line 10: The removeWords(myText, ...