Creating a custom categorized corpus in NLTK and Python

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Question :

Creating a custom categorized corpus in NLTK and Python

I’m experiencing a bit of a problem which has to do with regular expressions and CategorizedPlaintextCorpusReader in Python.

I want to create a custom categorized corpus and train a Naive-Bayes classifier on it. My issue is the following: I want to have two categories, “pos” and “neg”. The positive files are all in one directory, main_dir/pos/*.txt, and the negative ones are in a separate directory, main_dir/neg/*.txt.

How can I use the CategorizedPlaintextCorpusReader to load and label all the positive files in the pos directory, and do the same for the negative ones?

NB: The setup is absolutely the same as the Movie_reviews corpus (~nltk_datacorporamovie_reviews).

Asked By: TE0


Answer #1:

Here is the answer to my question.
Since I was thinking about using two cases I think it’s good to cover both in case someone needs the answer in the future.
If you have the same setup as the movie_review corpus – several folders labeled in the same way you would like your labels to be called and containing the training data you can use this.

reader = CategorizedPlaintextCorpusReader('~/MainFolder/', r'.*.txt', cat_pattern=r'(w+)/*')

The other approach that I was considering is putting everything in a single folder and naming the files 0_neg.txt, 0_pos.txt, 1_neg.txt etc. The code for your reader should look something like:

reader = CategorizedPlaintextCorpusReader('~/MainFolder/', r'.*.txt', cat_pattern=r'd+_(w+).txt')

I hope that this would help someone in the future.

Answered By: TE0

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