使用NLTK / Python中的电影评论语料库进行分类

时间：2014-01-14 06:08:11

标签： python nlp nltk sentiment-analysis corpus

我希望以NLTK Chapter 6的方式进行一些分类。这本书似乎跳过了创建类别的一步，我不确定我做错了什么。我的脚本在这里，响应如下。我的问题主要源于第一部分 - 基于目录名称的类别创建。这里的一些其他问题使用了文件名（即pos_1.txt和neg_1.txt），但我更愿意创建可以将文件转储到的目录。

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)

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!

--------- UPDATE ------------- 感谢alvas的详细解答！不过，我有两个问题。

  1. 是否可以像我试图那样从文件名中获取类别？我希望与review_pos.txt方法一样，只从文件夹名称而不是文件名中抓取pos。

  2. 我运行了您的代码，但在

    上遇到语法错误

    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:]]

    3. 第一个for下的胡萝卜。我是一名初学者Python用户，我对这段语法不太熟悉，试图将其添加到其中。

    ----更新2 ---- 错误是

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

1 个答案:

答案 0 :(得分：14)

是的，第6章的教程旨在为学生提供基础知识，并且从那里开始，学生应该通过探索NLTK中的可用内容以及不可用的内容来构建。所以让我们一次解决一个问题。

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

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，我喜欢一个元组列表，其中第一个元素是.txt文件中的单词列表，第二个是类别。虽然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。您的代码没有任何问题，只是您应该尝试使用命名空间（请参阅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

<强> [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打印了FreqDist，因此错误似乎没有nltk_data/目录中的文件。

您拥有fic/11.txt的事实表明您正在使用某些旧版本的NLTK或NLTK语料库。通常fileids中的movie_reviews以pos / neg开头，然后是斜线，然后是文件名，最后是.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()

然后确保在语料库标签下正确下载了电影评论语料库：

MR dl

回到代码，循环浏览电影评论语料库中的所有单词似乎是多余的，如果您已经在文档中过滤了所有单词，那么我宁愿这样做以提取所有功能集：

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，通过功能拆分火车/测试是可以的，但我认为最好使用文档，所以不要这样：

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

我建议改为：

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:]]

然后将数据输入分类器并瞧！所以这里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)

<强> [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
相关问题
最新问题