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在NLTK / Python中使用电影评论语料库进行分类

在NLTK / Python中使用电影评论语料库进行分类

慕无忌1623718 2019-11-19 11:00:54
我希望根据NLTK第6章进行分类。这本书似乎跳过了创建类别的步骤,而且我不确定自己做错了什么。我在这里有以下响应的脚本。我的问题主要来自第一部分-基于目录名称的类别创建。此处的其他一些问题使用了文件名(即pos_1.txt和neg_1.txt),但我希望创建可以将文件转储到的目录。from nltk.corpus import movie_reviewsreviews = CategorizedPlaintextCorpusReader('./nltk_data/corpora/movie_reviews', r'(\w+)/*.txt', cat_pattern=r'/(\w+)/.txt')reviews.categories()['pos', 'neg']documents = [(list(movie_reviews.words(fileid)), category)            for category in movie_reviews.categories()            for fileid in movie_reviews.fileids(category)]all_words=nltk.FreqDist(    w.lower()     for w in movie_reviews.words()     if w.lower() not in nltk.corpus.stopwords.words('english') and w.lower() not in  string.punctuation)word_features = all_words.keys()[:100]def document_features(document):     document_words = set(document)     features = {}    for word in word_features:        features['contains(%s)' % word] = (word in document_words)    return featuresprint document_features(movie_reviews.words('pos/11.txt'))featuresets = [(document_features(d), c) for (d,c) in documents]train_set, test_set = featuresets[100:], featuresets[:100]classifier = nltk.NaiveBayesClassifier.train(train_set)print nltk.classify.accuracy(classifier, test_set)classifier.show_most_informative_features(5)返回:File "test.py", line 38, in <module>    for w in movie_reviews.words()File "/usr/local/lib/python2.6/dist-packages/nltk/corpus/reader/plaintext.py", line 184, in words    self, self._resolve(fileids, categories))File "/usr/local/lib/python2.6/dist-packages/nltk/corpus/reader/plaintext.py", line 91, in words    in self.abspaths(fileids, True, True)])File "/usr/local/lib/python2.6/dist-packages/nltk/corpus/reader/util.py", line 421, in concat    raise ValueError('concat() expects at least one object!')ValueError: concat() expects at least one object!
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