Text Classification

很多时候，我们需要通过一些预先定义的标准将可用文本分类为不同的类别。 nltk提供此类功能作为各种语料库的一部分。 在下面的示例中，我们查看电影评论语料库并检查可用的分类。

# 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)

当我们运行上面的程序时，我们得到以下输出 -

['neg', 'pos']

现在让我们看一下带有正面评论的文件的内容。 这个文件中的句子是标记化的，我们打印前四个句子来查看样本。

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])

当我们运行上面的程序时，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，我们通过使用nltk中的FreqDist函数来标记每个文件中的单词并找到最常用的单词。

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))

当我们运行上面的程序时，我们得到以下输出 -

[(,', 77717), (the', 76529), (.', 65876), (a', 38106), (and', 35576), 
(of', 34123), (to', 31937), (u"'", 30585), (is', 25195), (in', 21822)]