情感分析正成为研究和社交媒体分析的热点领域，尤其是在用户评论和微博上。它是文本挖掘的一种特殊情况，一般关注在识别正反观点上，虽然它常不很准确，它仍然是有用的。为简单起见（因为训练数据容易获取），我将重点放在2个可能的情感分类：积极的和消极的。
def word_feats(words):
return dict([(word, True) for word in words])
这里是在电影评论语料上训练和测试朴素贝叶斯分类器的完整Python代码。
import nltk.classify.util
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews
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()
train on 1500 instances, test on 500 instances
accuracy: 0.728
Most Informative Features
magnificent = True pos : neg = 15.0 : 1.0
outstanding = True pos : neg = 13.6 : 1.0
insulting = True neg : pos = 13.0 : 1.0
vulnerable = True pos : neg = 12.3 : 1.0
ludicrous = True neg : pos = 11.8 : 1.0
avoids = True pos : neg = 11.7 : 1.0
uninvolving = True neg : pos = 11.7 : 1.0
astounding = True pos : neg = 10.3 : 1.0
fascination = True pos : neg = 10.3 : 1.0
idiotic = True neg : pos = 9.8 : 1.0
如你所见，10个最由信息量的特征是是，在大多数情况下，高度描述性的形容词。只有2个字，似乎有点奇怪是“弱势”和“避免”。也许这些词是表明一部好电影的重要的情节点或情节发展。无论是哪种情况，用简单的假设和非常少的代码，我们能够得到几乎73％的准确率。这有点接近人类的准确性，显然人们认同的情绪时候只有大约80％。在本系列的后续文章将介绍精度和召回指标，替代的分类，技术提高精度。
原文：http://streamhacker.com/2010/05/10/text-classification-sentiment-analysis-naive-bayes-classifier/
原文地址：http://blog.csdn.net/laozhaokun/article/details/37937337
