example for document classify use nltk and python

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

>>> 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:

>>> 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:

>>> 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