python-2.7 - 在NLTK中使用我自己的语料库而不是movie_reviews语料库进行分类

Question

我使用以下代码并从 Classification using movie review corpus in NLTK/Python 中得到它

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)

输出:

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

我想创建我自己的文件夹而不是 movie_reviews在 nltk 中，并将我自己的文件放入其中。

Answer 1

如果您的数据结构与 movie_review 完全相同NLTK 中的语料库，有两种方法可以“破解”你的方式:

1. 将您的语料库目录放入您保存 nltk.data 的位置

首先检查您的nltk.data在哪里保存:

>>> import nltk
>>> nltk.data.find('corpora/movie_reviews')
FileSystemPathPointer(u'/home/alvas/nltk_data/corpora/movie_reviews')

然后将你的目录移动到 nltk_data/corpora所在的位置已保存:

# Let's make a test corpus like `nltk.corpus.movie_reviews`
~$ mkdir my_movie_reviews
~$ mkdir my_movie_reviews/pos
~$ mkdir my_movie_reviews/neg
~$ echo "This is a great restaurant." > my_movie_reviews/pos/1.txt
~$ echo "Had a great time at chez jerome." > my_movie_reviews/pos/2.txt
~$ echo "Food fit for the ****" > my_movie_reviews/neg/1.txt
~$ echo "Slow service." > my_movie_reviews/neg/2.txt
~$ echo "README please" > my_movie_reviews/README
# Move it to `nltk_data/corpora/`
~$ mv my_movie_reviews/ nltk_data/corpora/

在你的python代码中:

>>> import string
>>> from nltk.corpus import LazyCorpusLoader, CategorizedPlaintextCorpusReader
>>> from nltk.corpus import stopwords
>>> my_movie_reviews = LazyCorpusLoader('my_movie_reviews', CategorizedPlaintextCorpusReader, r'(?!\.).*\.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
>>> mr = my_movie_reviews
>>>
>>> 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()]
>>> for i in documents:
...     print i
... 
([u'Food', u'fit', u'****'], u'neg')
([u'Slow', u'service'], u'neg')
([u'great', u'restaurant'], u'pos')
([u'great', u'time', u'chez', u'jerome'], u'pos')

(有关更多详细信息，请参阅 https://github.com/nltk/nltk/blob/develop/nltk/corpus/util.py#L21 和 https://github.com/nltk/nltk/blob/develop/nltk/corpus/init.py#L144 )

2. 创建您自己的 CategorizedPlaintextCorpusReader

如果您无权访问 nltk.data目录并且你想使用你自己的语料库，试试这个:

# Let's say that your corpus is saved on `/home/alvas/my_movie_reviews/`

>>> import string; from nltk.corpus import stopwords
>>> from nltk.corpus import CategorizedPlaintextCorpusReader
>>> mr = CategorizedPlaintextCorpusReader('/home/alvas/my_movie_reviews', r'(?!\.).*\.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
>>> 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()]
>>> 
>>> for doc in documents:
...     print doc
... 
([u'Food', u'fit', u'****'], 'neg')
([u'Slow', u'service'], 'neg')
([u'great', u'restaurant'], 'pos')
([u'great', u'time', u'chez', u'jerome'], 'pos')

类似问题已在 Creating a custom categorized corpus in NLTK and Python 上提出和 Using my own corpus for category classification in Python NLTK

这是可以工作的完整代码:

import string
from itertools import chain

from nltk.corpus import stopwords
from nltk.probability import FreqDist
from nltk.classify import NaiveBayesClassifier as nbc
from nltk.corpus import CategorizedPlaintextCorpusReader
import nltk

mydir = '/home/alvas/my_movie_reviews'

mr = CategorizedPlaintextCorpusReader(mydir, r'(?!\.).*\.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
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)