Python 文本分类
很多时候,我们需要根据一些预定义的标准将可用的文本分为不同的类别。nltk提供了各种corpora的功能。在下面的示例中,我们会查看电影评论corpus,并检查可用的分类。
# Lets See how the movies are classified
from nltk.corpus import movie_reviews
all_cats = []
for w in movie_reviews.categories():
all_cats.append(w.lower())
print(all_cats)
当我们运行上述程序时,得到以下输出−。
['neg', 'pos']
现在让我们看一下其中一个正面评论的内容。这个文件中的句子已经进行了分词,并且我们打印出前四个句子来查看示例。
from nltk.corpus import movie_reviews
from nltk.tokenize import sent_tokenize
fields = movie_reviews.fileids()
sample = movie_reviews.raw("pos/cv944_13521.txt")
token = sent_tokenize(sample)
for lines in range(4):
print(token[lines])
当我们运行上述程序时,我们得到以下输出 –
meteor threat set to blow away all volcanoes & twisters !
summer is here again !
this season could probably be the most ambitious = season this decade with hollywood churning out films
like deep impact , = godzilla , the x-files , armageddon , the truman show ,
all of which has but = one main aim , to rock the box office .
leading the pack this summer is = deep impact , one of the first few film
releases from the = spielberg-katzenberg-geffen's dreamworks production company .
接下来,我们对这些文件中的单词进行切词,并使用nltk中的FreqDist函数来找到最常见的单词。
import nltk
from nltk.corpus import movie_reviews
fields = movie_reviews.fileids()
all_words = []
for w in movie_reviews.words():
all_words.append(w.lower())
all_words = nltk.FreqDist(all_words)
print(all_words.most_common(10))
当我们运行上述程序时,我们会得到以下的输出 –
[(,', 77717), (the', 76529), (.', 65876), (a', 38106), (and', 35576),
(of', 34123), (to', 31937), (u"'", 30585), (is', 25195), (in', 21822)]