如果您的数据与结构完全相同movie_review
NLTK 中的语料库,有两种方法可以“破解”您的方式:
1. 将你的语料库目录放入你保存的位置nltk.data
首先检查你在哪里nltk.data
saved:
>>> 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所在位置nltk_data/corpora
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 https://github.com/nltk/nltk/blob/develop/nltk/corpus/util.py#L21 and https://github.com/nltk/nltk/blob/develop/nltk/corpus/init.py#L144 https://github.com/nltk/nltk/blob/develop/nltk/corpus/__init__.py#L144)
2. 创建你自己的CategorizedPlaintextCorpusReader
如果您无法访问nltk.data
目录并且您想使用自己的语料库,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')
类似的问题已被问到使用 NLTK 和 Python 创建自定义分类语料库 https://stackoverflow.com/questions/10463898/creating-a-custom-categorized-corpus-in-nltk-and-python and 在Python NLTK中使用我自己的语料库进行类别分类 https://stackoverflow.com/questions/8818265/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)