I have text data which is crawled from websites. I am preprocessing data to train Word2Vec model. Should I remove stopwords and do lemmatization? How to preprocess data for Word2Vec?

  • $\begingroup$ Different libraries require different preprocessing steps. What are you working with? $\endgroup$
    – Leevo
    Commented Feb 13, 2020 at 13:16
  • $\begingroup$ I am using gensim but open to try different libraries too. @Leevo $\endgroup$
    – nehiridil
    Commented Feb 13, 2020 at 13:24

1 Answer 1


Welcome to the community,

I do not know about other libraries, but gensim has a very good API to create word2vec models. In order to preprocess data, you have to decide first what things you are gonna keep in your vocab and whatnot. for ex:- Punctuations, numbers, alphanumeric words(ex - 42nd) etc.

In my knowledge, the most generic preprocessing pipeline is the following:-

1) Convert to lower 2) Remove punctuations/symbols/numbers (but it is your choice) 3) Normalize the words (lemmatize and stem the words)

Once this is done, now you can tokenize the sentence into uni/bi/tri-grams.

Have a look at this

The generic format to put data in gensim.models.word2vec()'s sentence parameter is : [[tokeneized sentence 1], [tokenized sentence 2].....and so on]

Hope it helps, thanks!!

  • $\begingroup$ great if this question solves your problem, kindly accept this answer as solution so that the admin can take it off from the open questions domain. Thanks!! $\endgroup$ Commented Feb 14, 2020 at 18:00
  • $\begingroup$ I tried 2 versions of it, one with normalized words another without normalizing. In my case, without normalization model works better. Thanks! $\endgroup$
    – nehiridil
    Commented Feb 16, 2020 at 9:23
  • $\begingroup$ nice, please have look at this to accept this solution as asnwer. stackoverflow.com/help/accepted-answer $\endgroup$ Commented Feb 17, 2020 at 7:39

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