使用 NLTK/Python 中的电影评论语料库进行分类

问题描述：

我想按照NLTK 第 6 章的思路进行分类。这本书似乎跳过了创建类别的步骤，我不确定我做错了什么。我这里有我的脚本，响应如下。我的问题主要来自第一部分——基于目录名称创建类别。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)

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 alvas 的详细回答！不过我有两个问题。

1. 是否可以像我尝试的那样从文件名中获取类别？我希望以与方法相同的方式执行此操作，仅从文件夹名称而不是文件名中review_pos.txt获取。pos

2. 我运行了你的代码，遇到了语法错误

`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:]]`

第一个是胡萝卜for。我是 Python 初学者，对那段语法还不够熟悉，无法尝试解决它。

----更新 2---- 错误是

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

解决方案 1：

是的，第 6 章的教程旨在让学生掌握基础知识，然后学生应该在此基础上探索 NLTK 中有哪些可用功能和哪些不可用功能。所以让我们逐一解决这些问题。

首先，通过目录获取“pos”/“neg”文档的方式很可能是正确的做法，因为语料库就是这样组织的。

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.

[出去]：

['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，第二个元素是类别。同时删除停用词和标点符号：

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 处的错误FreqDist(for w in movie_reviews.words() ...)。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

[输出]：

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

由于上述代码FreqDist正确打印，错误似乎是因为您没有nltk_data/目录中的文件。

事实上，您fic/11.txt使用的是较旧版本的 NLTK 或 NLTK 语料库。通常，fileids中的movie_reviews以pos/开头，neg然后是斜杠，然后是文件名，最后是.txt，例如pos/cv001_18431.txt。

因此我认为，也许你应该使用以下命令重新下载文件：

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

然后确保在语料库选项卡下正确下载了电影评论语料库：

回到代码，如果你已经在文档中过滤了所有单词，那么循环遍历电影评论语料库中的所有单词似乎是多余的，所以我宁愿这样做来提取所有特征集：

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，按特征划分训练/测试是可以的，但我认为最好使用文档，因此不要这样做：

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

我建议你这样做：

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，瞧！以下是没有注释和演练的代码：

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)

[出去]：

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