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PONLTK classify interface using trained classifier
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  1. PONLTK classify interface using trained classifier
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    copied!<p>I have this little chunk of code I found <a href="http://streamhacker.com/2010/05/10/text-classification-sentiment-analysis-naive-bayes-classifier/" rel="nofollow">here</a>:</p> <pre><code>import nltk.classify.util from nltk.classify import NaiveBayesClassifier from nltk.corpus import movie_reviews from nltk.corpus import stopwords def word_feats(words): return dict([(word, True) for word in words]) negids = movie_reviews.fileids('neg') posids = movie_reviews.fileids('pos') negfeats = [(word_feats(movie_reviews.words(fileids=[f])), 'neg') for f in negids] posfeats = [(word_feats(movie_reviews.words(fileids=[f])), 'pos') for f in posids] negcutoff = len(negfeats)*3/4 poscutoff = len(posfeats)*3/4 trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff] testfeats = negfeats[negcutoff:] + posfeats[poscutoff:] print 'train on %d instances, test on %d instances' % (len(trainfeats), len(testfeats)) classifier = NaiveBayesClassifier.train(trainfeats) print 'accuracy:', nltk.classify.util.accuracy(classifier, testfeats) classifier.show_most_informative_features() </code></pre> <p>But how can I classify a random word that might be in the corpus.</p> <pre><code>classifier.classify('magnificent') </code></pre> <p>Doesn't work. Does it need some kind of object?</p> <p>Thank you very much.</p> <p>EDIT: Thanks to @unutbu's feedback and some digging <a href="http://nltk.googlecode.com/svn/trunk/doc/api/nltk.probability.ProbDistI-class.html#samples" rel="nofollow">here</a> and reading the comments on the original post the following yields 'pos' or 'neg' for this code (this one's a 'pos')</p> <pre><code>print(classifier.classify(word_feats(['magnificent']))) </code></pre> <p>and this yields the evaluation of the word for 'pos' or 'neg'</p> <pre><code>print(classifier.prob_classify(word_feats(['magnificent'])).prob('neg')) </code></pre>
 

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