Python NLTK Classifier.train(trainfeats)... ValueError : need more than 1 value to unpack

Question

def word_feats(words):
     return dict([(word, True) for word in words])

for tweet in negTweets:
     words = re.findall(r"[\w']+|[.,!?;]", tweet) #splits the tweet into words
     negwords = [(word_feats(words), 'neg')] #tag the words with feature
     negfeats.append(negwords) #add the words to the feature list
for tweet in posTweets:
     words = re.findall(r"[\w']+|[.,!?;]", tweet)
     poswords = [(word_feats(words), 'pos')]
     posfeats.append(poswords)

negcutoff = len(negfeats)*3/4 #take 3/4ths of the words
poscutoff = len(posfeats)*3/4

trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff] #assemble the train set
testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]

classifier = NaiveBayesClassifier.train(trainfeats)
print 'accuracy:', nltk.classify.util.accuracy(classifier, testfeats)
classifier.show_most_informative_features()

运行此代码时出现以下错误...

File "C:\Python27\lib\nltk\classify\naivebayes.py", line 191, in train

for featureset, label in labeled_featuresets:

ValueError: need more than 1 value to unpack

该错误来自 classifier = NaiveBayesClassifier.train(trainfeats) 行，我不确定为什么。我以前做过类似的事情，我的 trainfeats 接缝的格式与当时的格式相同...下面列出了该格式的示例...

[[({'me': True, 'af': True, 'this': True, 'joy': True, 'high': True, 'hookah': True, 'got': True}, 'pos')]]

我的 trainfeats 还需要什么其他值来创建分类器？强调文本

Answer 1

@Prune 的评论是正确的:您的 labeled_featuresets 应该是一系列对(二元素列表或元组):每个数据点的特征字典和类别。相反，trainfeats 中的每个元素都是一个包含一个元素的列表:这两个元素的元组。失去两个功能构建循环中的方括号，这部分应该可以正常工作。例如，

negwords = (word_feats(words), 'neg')
negfeats.append(negwords)

还有两件事:考虑使用 nltk.word_tokenize() 而不是进行自己的标记化。并且您应该随机化训练数据的顺序，例如与random.scramble(trainfeats)。