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- import nltk
- 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)
- all_words = []
- for w in movie_reviews.words():
- all_words.append(w.lower())
- all_words = nltk.FreqDist(all_words)
- word_features = list(all_words.keys())[:3000]
- def find_features(document):
- words = set(document)
- features = {}
- for w in word_features:
- features[w] = (w in words)
- return features
- featuresets = [(find_features(rev), category) for (rev, category) in documents]
- training_set = featuresets[:1900]
- testing_set = featuresets[1900:]
- classifier = nltk.NaiveBayesClassifier.train(training_set)
- print("Classifier accuracy percent:",(nltk.classify.accuracy(classifier, testing_set))*100)
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