Stopword removal with NLTK

Question

I am trying to process a user entered text by removing stopwords using nltk toolkit, but with stopword-removal the words like 'and', 'or', 'not' gets removed. I want these words to be present after stopword removal process as they are operators which are required for later processing text as query. I don't know which are the words which can be operators in text query, and I also want to remove unnecessary words from my text.

Answer 1

There is an in-built stopword list in NLTK made up of 2,400 stopwords for 11 languages (Porter et al), see http://nltk.org/book/ch02.html

>>> from nltk import word_tokenize >>> from nltk.corpus import stopwords >>> stop = set(stopwords.words('english')) >>> sentence = "this is a foo bar sentence" >>> print([i for i in sentence.lower().split() if i not in stop]) ['foo', 'bar', 'sentence'] >>> [i for i in word_tokenize(sentence.lower()) if i not in stop]  ['foo', 'bar', 'sentence'] 

I recommend looking at using tf-idf to remove stopwords, see Effects of Stemming on the term frequency?

Answer 2

I suggest you create your own list of operator words that you take out of the stopword list. Sets can be conveniently subtracted, so:

operators = set(('and', 'or', 'not')) stop = set(stopwords...) - operators 

Then you can simply test if a word is in or not in the set without relying on whether your operators are part of the stopword list. You can then later switch to another stopword list or add an operator.

if word.lower() not in stop:     # use word