example for document classify use nltk and python

1. get the movie comment and classify it into pos or neg
    code like below:

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>>> import nltki
>>> import random
>>> from nltk.corpus import movie_reviews
>>> documents
= [(list(movie_reviews.words(fileid)),
category)
...
for category in movie_reviews.categories()
...
for fileid in movie_reviews.fileids(category)]
>>> random.shuffle(documents)

2.get the features of the documents, that if the word in the selected document
    code like below:

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>>> all_words
= nltk.FreqDist(w.lower()
for w in movie_reviews.words())
>>> word_features
= all_words.keys()[:2000]
>>> def document_features(document):
... document_words
= set(document)
... features
= {}
...
for word 
in word_features:
... features['contains(%s)' %
word] 
= (word
in document_words)
... return features

3.train and test the classifier for the document
    code like below:

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>>> featuresets
= [(document_features(d), c)
for (d,c)
in documents]
>>> train_set, test_set
= featuresets[100:], featuresets[:100]
>>> classifier
= nltk.NaiveBayesClassifier.train(train_set)
>>> print nltk.classify.accuracy(classifier,
test_set)
0.73