Tips for Constructing Custom Stop Word Lists

Stop words are a set of commonly used words in any language. For example, in English, “the”, “is” and “and”, would easily qualify as stop words. In NLP and text mining applications, stop words are used to eliminate unimportant words, allowing applications to focus on the important words instead.

While it is fairly easy to use a published set of stop words, in many cases, using such stop words is completely insufficient for certain applications. For example, in clinical texts, terms like “mcg” “dr.” and “patient” occur almost in every document that you come across. So, these terms may be regarded as potential stop words for clinical text mining and retrieval. Similarly, for tweets, terms like “#” “RT”, “@username” can be potentially regarded as stop words. The common language specific stop word list generally DOES NOT cover such domain specific terms.

The good news is that it is actually fairly easy to construct your own domain specific stop word list. Here are a few ways of doing it assuming you have a large corpus of text from the domain of interest, you can do one or more of the following to figure out your stop words:

1. Most frequent terms as stop words

Sum the term frequencies of each unique word, w across all documents in your collection. Sort the terms in descending order of raw term frequency. You can take the top N terms to be your stop words. You can also eliminate common English words (using a publish stop list) prior to sorting so that you are sure that you target the domain specific stop words. Another option is to treat words occurring in more X% of your documents as stop words. I have personally found eliminating words that appear in 85% of documents to be effective in several text mining tasks.  The benefit of this approach is that it is really easy implement, the downside however is if you have a particularly long document, the raw term frequency from just a few documents can dominate and cause the term to be at the top. One way to resolve this is to normalize the raw term frequency using a normalizer such as the document length (i.e. number of words in a given document).

2. Least frequent terms as stop words

Just as terms that are extremely frequent could be distracting terms rather than discriminating terms, terms that are extremely infrequent may also not be useful for text mining and retrieval. For example the username “@username” that occurs only once in a collection of tweets, may not be very useful. Other terms like “yoMateZ!” which could be just made-up terms by people again may not be useful for text mining applications. Note that certain terms like “yaaaaayy!!” can often be normalized to standard forms such as “yay”. However, despite all the normalization if terms still have a term frequency count of one you could remove it. This could significantly reduce your overall feature space.

3. Low IDF terms as stop words

Inverse document frequency (IDF) basically refers to the inverse fraction of documents in your collection that contains a specific term ti. Let us say you have N documents. And term ti occurred in M of the N documents. The IDF of ti is thus computed as:

IDF(ti)=Log N/M

So the more documents ti appears in, the lower the IDF score. This means terms that appear in each and every document will have an IDF score of 0. If you rank each ti in your collection by its IDF score in descending order, you can treat the bottom K terms with the lowest IDF scores to be your stop words. Again, you can also eliminate common English words (using a published stop list) prior to sorting so that you are sure that you target the domain specific low IDF words. This is not necessary really if your K is large enough such that it will prune both general stop words as well as domain specific stop words. You will find more information about IDFs here.

So, would stop words help my task?

So how would you know if removing domain specific stop words would be helpful in your case? Easy, test it on a subset of your data. See if whatever measure of accuracy and performance improves, stays constant or degrades. If it degrades, needless to say, don’t do it unless the degradation is negligible and you see gains in other forms such as decrease in size of model, ability to process things in memory, and etc.

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5 thoughts on “Tips for Constructing Custom Stop Word Lists”

  1. For most frequent words as stop words, is the max_df argument in sklearn TFIDF vectorizer performing the same function?

    1. Well yes and no. In this example, I was referring to `term frequency`. `max_df` looks at `document frequency`.
      Thresholding on the two can have the same effect though – that is to eliminate frequently occurring words.

  2. Pingback: What are Stop Words? | Opinosis Analytics

  3. What do you think about also keeping some words, that would normally be stopwords?
    E.g. normally “not” would be a stop word. But in a scenario where you want to look for important phrases, and don’t want things like “not good service” to get lost.

    1. Yes, you can keep those depending on how important it is for your task. It looks like you are mostly dealing with multi-word expressions for some kind of sentiment analysis? If so, you should certainly not get rid of the sentiment context.

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