Question

我使用以下代码，我从Classification using movie review corpus in NLTK/Python

获取它

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

我想在nltk中创建自己的文件夹而不是movie_reviews，并将自己的文件放在其中。

Answer 1

如果您的数据与NLTK中的movie_review语料库完全相同，那么有两种方法可以“破解”您的方式：

<强> 1。将您的语料库目录放入保存nltk.data

的位置

首先检查nltk.data保存在哪里：

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

然后将您的目录移动到保存nltk_data/corpora的位置：

# 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）

<强> 2。创建自己的CategorizedPlaintextCorpusReader

如果您无法访问nltk.data目录，并且想要使用自己的语料库，请尝试以下操作：

# 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')

在Creating a custom categorized corpus in NLTK and Python和Using my own corpus for category classification in Python NLTK

上提出了类似的问题

以下是完整的代码：

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)

使用我自己的语料库而不是movie_reviews语料库在NLTK中进行分类

1 个答案: