Python Text Processing 简明教程

Python - Text Classification

很多时候，我们需要按照一些预定义标准将可用文本分成各种类别。nltk 提供此类功能作为各种语料库的一部分。在下例中，我们查看了电影评论语料库并查看了可用的类别。

# 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)

当我们运行以上程序时，我们得到以下输出：

['neg', 'pos']

现在，我们来看看其中一个包含正面评论的文件的内容。此文件中的句子会被标记，我们打印前四个句子以查看样本。

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])

当我们运行以上程序时，我们得到了以下输出 −

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 .

在接下来，我们在这些文件中的每个文件对单词进行标记，并使用 nltk 的 FreqDist 函数找出最常见的单词。

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))

当我们运行以上程序时，我们得到了以下输出 −

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