for fileid in movie_reviews.fileids(category)]
random.shuffle(documents)
allWords = []
for w in movie_reviews.words():
allWords.append(w.lower())
allWords = nltk.FreqDist(allWords)
wordFeatures = list(allWords.keys())[:3000]
def findFeatures(document):
words = set(document)
features = {}
for w in wordFeatures:
features[w] = (w in words)
return features
#print((findFeatures(movie_reviews.words('neg/cv000_29416.txt'))))
featureSets = [(findFeatures(rev), category) for (rev, category) in documents ]
new_training_set = featureSets[:100]
testing_set = featureSets[100:]
cl = NaiveBayesClassifier(new_training_set)
print(cl.accuracy(testing_set))
def updateNaiveBayes():
cl = NaiveBayesClassifier(new_training_set)
print(cl.accuracy(testing_set))
