Remove common words using nltk

Remove common words and stopwords from the data sets.

Posted by ourren on January 15, 2015

The current topic is that: I want to remove the common words from my sample data. But I have searched from the Internet, it did not have similar post. So I want to share some tips about this topic. Since I’m doing some research with NLP, and nltk is the best package with english.

common words

the first question is how to define the common words? Maybe we can think in two ways: the first is the most often used words on the Internet, dictionary, or books; the second is the most high frequency words in the sample data. Actually it is all depend on your situation.


the nltk have a corpus called stopwords, it means: >There is also a corpus of stopwords, that is, high-frequency words like the, to and also that we sometimes want to filter out of a document before further processing. Stopwords usually have little lexical content, and their presence in a text fails to distinguish it from other texts.

how to get the common words list, it’s very simple, just as below:

from nltk.corpus import stopwords	
english_stopwords = stopwords.words('english')
print len(english_stopwords)

but the length of stopwords is very small, only (127 words).

high frequency in sample

if the above methods could not solve the problem in our research, we need to create our own often words. Here is my current method:

  1. convert the sample data to word list;
  2. calculate the frequency of the word list;
  3. get the top frequency of the list, this is the common words.

the python code is below:

import nltk
def convertlist(filename):
    list_out = list()
    fhandle = open(filename)
    for line in fhandle.readlines():
        line = line.replace("\n", "").strip()
        line = line.lower()
            texts_tokenized = word_tokenize(line)
            for words in texts_tokenized:
                english_punctuations = [',', '.', ':', ';', '?', '(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%']
                if not words in english_punctuations:
    return list_out

def get_common_words():
    ret_list = []
    out_path = "./sample_data"
    content = convertlist(out_path)
    fdist2 = nltk.FreqDist(content)
    most_list = fdist2.most_common(400)
    for x, value in most_list:
    return ret_list
def main():
	common_list = get_common_words()
	print common_list[0:100]

if __name__ == '__main__':