Stops Words

Stop words are words that are filtered out before or after the processing of natural language data (text). Though “stop words” usually refer to the most common words in a language, there are no definitive stop word lists. Stop words may be common, but they carry little meaning – excluding them from analysis often improves results.

Examples of Stop Words

The following are examples of stop words in the English language:

“a”, “about”, “above”, “after”, “again”, “against”, “all”, “am”, “an”, “and”,


“between”,”both”,”but”,”by”,”can’t”, “cannot”,”could”,”couldn’t”,”did”, “didn’t”.

Stop Words Removal Tools

Different tools use different lists of stop words. Some common stop word removal tools are:

  • NLTK (Natural Language Toolkit): NLTK is a python library that comes with a pre-defined set of stop words (about 150) for multiple languages.
  • Stop Word Filter: This is a Java-based tool that uses a list of stop words.
  • Snowball: Snowball is a small string processing language designed for use in Information Retrieval. It has a list of stop words for multiple languages.
  • R: R has a package called tm (text mining) that includes a set of stop words for multiple languages.

Advantages of Using Stop Words

There are a few advantages to using stop words:

  • It can help improve the results of your text analytics by removing common, meaningless words.
  • It can make your text analytics more efficient by reducing the amount of data that needs to be processed.

Disadvantages of Using Stop Words

Stop words also have a few disadvantages:

  • They can remove important context from your data. For example, the word “not” is a stop word, so if you are trying to analyze sentiment and the text includes the phrase “not good”, the stop word removal would change the meaning of the phrase.
  • They can create issues with homonyms. For example, the word “fly” could be removed as a stop word, but then the text would lose its meaning if it included the phrase “fly fishing”.

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