sentimentAnalyzer.py

import re, math, collections, itertools
import nltk, nltk.classify.util, nltk.metrics
from nltk.classify import NaiveBayesClassifier
from nltk.metrics import BigramAssocMeasures
from nltk.probability import FreqDist, ConditionalFreqDist

def evaluate_features(feature_select):
    #reading pre-labeled input and splitting into lines
    posSentences = open('polarityData\\rt-polaritydata\\rt-polarity-pos.txt', 'r')
    negSentences = open('polarityData\\rt-polaritydata\\rt-polarity-neg.txt', 'r')
    posSentences = re.split(r'\n', posSentences.read())
    negSentences = re.split(r'\n', negSentences.read())
 
    posFeatures = []
    negFeatures = []
    #http://stackoverflow.com/questions/367155/splitting-a-string-into-words-and-punctuation
    #breaks up the sentences into lists of individual words (as selected by the input mechanism) and appends 'pos' or 'neg' after each list
    for i in posSentences:
        posWords = re.findall(r"[\w']+|[.,!?;]", i)
        posWords = [feature_select(posWords), 'pos']
        posFeatures.append(posWords)
    for i in negSentences:
        negWords = re.findall(r"[\w']+|[.,!?;]", i)
        negWords = [feature_select(negWords), 'neg']
        negFeatures.append(negWords)



	posCutoff = int(math.floor(len(posFeatures)*3/4))
	negCutoff = int(math.floor(len(negFeatures)*3/4))
	trainFeatures = posFeatures[:posCutoff] + negFeatures[:negCutoff]
	testFeatures = posFeatures[posCutoff:] + negFeatures[negCutoff:]


	classifier = NaiveBayesClassifier.train(trainFeatures)

	for i, (features, label) in enumerate(testFeatures):
		referenceSets[label].add(i)
		predicted = classifier.classify(features)
		testSets[predicted].add(i)


    print 'train on %d instances, test on %d instances' % (len(trainFeatures), len(testFeatures))
    print 'accuracy:', nltk.classify.util.accuracy(classifier, testFeatures)
    print 'pos precision:', nltk.metrics.precision(referenceSets['pos'], testSets['pos'])
    print 'pos recall:', nltk.metrics.recall(referenceSets['pos'], testSets['pos'])
    print 'neg precision:', nltk.metrics.precision(referenceSets['neg'], testSets['neg'])
    print 'neg recall:', nltk.metrics.recall(referenceSets['neg'], testSets['neg'])
    print 'most informative:',classifier.show_most_informative_features(10)