Naive Bayes Bayes Error

Question

Hey, I'm trying to use the Naive Bayes classifier to classify some text. I am using NLTK. Whenever I test a classifier using the classify () method, it always returns the correct classification for the first element and the same classification for every other line of text that I am classifying. Below is my code:

from nltk.corpus import movie_reviews from nltk.tokenize import word_tokenize import nltk import random import nltk.data 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 = nltk.FreqDist(w.lower() for w in movie_reviews.words()) word_features = all_words.keys()[:2000] def bag_of_words(words): return dict([word,True] for word in words) def document_features(document): document_words = set(document) features = {} for word in word_features: features['contains(%s)' % word] = (word in document_words) return features featuresets = [(document_features(d), c) for (d,c) in documents] train_set, test_set = featuresets[100:], featuresets[:100] classifier = nltk.NaiveBayesClassifier.train(train_set) text1="i love this city" text2="i hate this city" feats1=bag_of_words(word_tokenize(text1)) feats2=bag_of_words(word_tokenize(text2)) print classifier.classify(feats1) print classifier.classify(feats2) 

This code will print the word twice, as if I turned the last 2 lines of the code, it will print neg twice. Can anyone help?

Answer 1

Edit

 features['contains(%s)' % word] = (word in document_words) 

to

 features[word] = (word in document) 

Otherwise, the classifier knows only about the "words" of the form "contains (...)" and therefore does not know the words in "i love this city"

---

 import nltk.tokenize as tokenize import nltk import random random.seed(3) def bag_of_words(words): return dict([word, True] for word in words) def document_features(document): features = {} for word in word_features: features[word] = (word in document) # features['contains(%s)' % word] = (word in document_words) return features movie_reviews = nltk.corpus.movie_reviews documents = [(set(movie_reviews.words(fileid)), category) for category in movie_reviews.categories() for fileid in movie_reviews.fileids(category)] random.shuffle(documents) all_words = nltk.FreqDist(w.lower() for w in movie_reviews.words()) word_features = all_words.keys()[:2000] train_set = [(document_features(d), c) for (d, c) in documents[:200]] classifier = nltk.NaiveBayesClassifier.train(train_set) classifier.show_most_informative_features() for word in ('love', 'hate'): # No hope in passing the tests if word is not in word_features assert word in word_features print('probability {w!r} is positive: {p:.2%}'.format( w = word, p = classifier.prob_classify({word : True}).prob('pos'))) tests = ["i love this city", "i hate this city"] for test in tests: words = tokenize.word_tokenize(test) feats = bag_of_words(words) print('{s} => {c}'.format(s = test, c = classifier.classify(feats))) 

gives

 Most Informative Features worst = True neg : pos = 15.5 : 1.0 ridiculous = True neg : pos = 11.5 : 1.0 batman = True neg : pos = 7.6 : 1.0 drive = True neg : pos = 7.6 : 1.0 blame = True neg : pos = 7.6 : 1.0 terrible = True neg : pos = 6.9 : 1.0 rarely = True pos : neg = 6.4 : 1.0 cliches = True neg : pos = 6.0 : 1.0 $ = True pos : neg = 5.9 : 1.0 perfectly = True pos : neg = 5.5 : 1.0 probability 'love' is positive: 61.52% probability 'hate' is positive: 36.71% i love this city => pos i hate this city => neg