NLTK中词袋朴素贝叶斯分类器的实现

Answer 1

scikit-learn有一个多项式朴素贝叶斯的实现，这是朴素贝叶斯在这种情况下的正确变体。不过，支持向量机(SVM)可能会更好。

正如Ken在评论中指出的，NLTK为scikit-learn分类器提供了一个很好的包装器。根据文档修改后，这里有一个有点复杂的方法，它进行TF-IDF加权，根据chi2统计信息选择1000个最佳特征，然后将其传递给多项式朴素贝叶斯分类器。（我打赌这有点笨拙，因为我不太熟悉NLTK或SCIKIT-LEARN。）

import numpy as np
from nltk.probability import FreqDist
from nltk.classify import SklearnClassifier
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline

pipeline = Pipeline([('tfidf', TfidfTransformer()),
                     ('chi2', SelectKBest(chi2, k=1000)),
                     ('nb', MultinomialNB())])
classif = SklearnClassifier(pipeline)

from nltk.corpus import movie_reviews
pos = [FreqDist(movie_reviews.words(i)) for i in movie_reviews.fileids('pos')]
neg = [FreqDist(movie_reviews.words(i)) for i in movie_reviews.fileids('neg')]
add_label = lambda lst, lab: [(x, lab) for x in lst]
classif.train(add_label(pos[:100], 'pos') + add_label(neg[:100], 'neg'))

l_pos = np.array(classif.classify_many(pos[100:]))
l_neg = np.array(classif.classify_many(neg[100:]))
print "Confusion matrix:\n%d\t%d\n%d\t%d" % (
          (l_pos == 'pos').sum(), (l_pos == 'neg').sum(),
          (l_neg == 'pos').sum(), (l_neg == 'neg').sum())

这是为我打印的：

Confusion matrix:
524     376
202     698