在NLTK / Python中使用电影评论语料库进行分类

我期待在NLTK第6章中做一些分类。 这本书似乎跳过了创build类别的一步,我不知道我做错了什么。 我有我的脚本在这里的回应如下。 我的问题主要来自第一部分 – 基于目录名称的类别创build。 这里的一些其他问题已经使用了文件名(即pos_1.txtneg_1.txt ),但我更喜欢创build可以转储文件的目录。

 from nltk.corpus import movie_reviews reviews = 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 features print 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) 

这返回:

 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! 

———更新————-感谢您的详细解答! 不过,我有两个问题。

  1. 是否有可能从我正在尝试做的文件名抓取类别? 我希望能够像review_pos.txt方法一样,只从文件夹名称而不是文件名中获取pos
  2. 我运行了你的代码,并且遇到了一个语法错误

    train_set =[({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents[:numtrain]] test_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents[numtrain:]]

用胡萝卜先for 。 我是一个初学Python的用户,对于这种语法来说,我不太熟悉。

—-更新2 —-错误是

 File "review.py", line 17 for i in word_features}, tag) ^ SyntaxError: invalid syntax` 

One Solution collect form web for “在NLTK / Python中使用电影评论语料库进行分类”

是的,第6章的教程是为学生提供基础知识的目标,学生应该通过探索NLTK中可用的内容来构build它。 所以我们一次一个地解决问题。

首先,通过目录获取“pos”/“neg”文档的方式很可能是正确的,因为语料库是按照这种方式组织的。

 from nltk.corpus import movie_reviews as mr from collections import defaultdict documents = defaultdict(list) for i in mr.fileids(): documents[i.split('/')[0]].append(i) print documents['pos'][:10] # first ten pos reviews. print print documents['neg'][:10] # first ten neg reviews. 

[OUT]:

 ['pos/cv000_29590.txt', 'pos/cv001_18431.txt', 'pos/cv002_15918.txt', 'pos/cv003_11664.txt', 'pos/cv004_11636.txt', 'pos/cv005_29443.txt', 'pos/cv006_15448.txt', 'pos/cv007_4968.txt', 'pos/cv008_29435.txt', 'pos/cv009_29592.txt'] ['neg/cv000_29416.txt', 'neg/cv001_19502.txt', 'neg/cv002_17424.txt', 'neg/cv003_12683.txt', 'neg/cv004_12641.txt', 'neg/cv005_29357.txt', 'neg/cv006_17022.txt', 'neg/cv007_4992.txt', 'neg/cv008_29326.txt', 'neg/cv009_29417.txt'] 

或者,我喜欢元组列表,其中第一个元素是.txt文件中的单词列表,第二个是类别 。 同时这样做也删除了停用词和标点符号:

 from nltk.corpus import movie_reviews as mr import string from nltk.corpus import stopwords stop = stopwords.words('english') documents = [([w for w in mr.words(i) if w.lower() not in stop and w.lower() not in string.punctuation], i.split('/')[0]) for i in mr.fileids()] 

接下来是FreqDist(for w in movie_reviews.words() ...)的错误FreqDist(for w in movie_reviews.words() ...) 。 你的代码没有问题,只是你应该尝试使用命名空间(参见http://en.wikipedia.org/wiki/Namespace#Use_in_common_languages )。 以下代码:

 from nltk.corpus import movie_reviews as mr from nltk.probability import FreqDist from nltk.corpus import stopwords import string stop = stopwords.words('english') all_words = FreqDist(w.lower() for w in mr.words() if w.lower() not in stop and w.lower() not in string.punctuation) print all_words 

[输出]:

 <FreqDist: 'film': 9517, 'one': 5852, 'movie': 5771, 'like': 3690, 'even': 2565, 'good': 2411, 'time': 2411, 'story': 2169, 'would': 2109, 'much': 2049, ...> 

由于上面的代码正确地打印了FreqDist ,这个错误就好像你没有在nltk_data/目录中的文件。

事实上,你有fic/11.txt表明你正在使用一些老版本的NLTK或NLTK语料库。 通常情况下, fileids中的movie_reviews ,开始pos / neg然后斜杠然后文件名和最后.txt ,例如pos/cv001_18431.txt

所以我想,也许你应该重新下载文件:

 $ python >>> import nltk >>> nltk.download() 

然后确保电影评论语料库正确下载在全集选项卡下:

MR dl

回到代码中,如果已经在文档中过滤了所有单词,则循环浏览电影评论语料库中的所有单词似乎是多余的,所以我宁愿这样做以提取所有function集:

 word_features = FreqDist(chain(*[i for i,j in documents])) word_features = word_features.keys()[:100] featuresets = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents] 

接下来,按function分解列车/testing是可以的,但我认为使用文档更好,所以不要这样做:

 featuresets = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents] train_set, test_set = featuresets[100:], featuresets[:100] 

我会推荐这个:

 numtrain = int(len(documents) * 90 / 100) train_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents[:numtrain]] test_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents[numtrain:]] 

然后将数据送入分类器,瞧! 所以这是没有评论和演练的代码:

 import string from itertools import chain from nltk.corpus import movie_reviews as mr from nltk.corpus import stopwords from nltk.probability import FreqDist from nltk.classify import NaiveBayesClassifier as nbc import nltk stop = stopwords.words('english') documents = [([w for w in mr.words(i) if w.lower() not in stop and w.lower() not in string.punctuation], i.split('/')[0]) for i in mr.fileids()] word_features = FreqDist(chain(*[i for i,j in documents])) word_features = word_features.keys()[:100] numtrain = int(len(documents) * 90 / 100) train_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents[:numtrain]] test_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents[numtrain:]] classifier = nbc.train(train_set) print nltk.classify.accuracy(classifier, test_set) classifier.show_most_informative_features(5) 

[OUT]:

 0.655 Most Informative Features bad = True neg : pos = 2.0 : 1.0 script = True neg : pos = 1.5 : 1.0 world = True pos : neg = 1.5 : 1.0 nothing = True neg : pos = 1.5 : 1.0 bad = False pos : neg = 1.5 : 1.0 
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