文字分類

很多時候，需要通過一些預先定義的標準將可用文字分類為各種類別。 nltk提供此類功能作為各種語料庫的一部分。 在下面的範例中，檢視電影評論語料庫並檢查可用的分類。

# 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)

當執行上面的程式時，我們得到以下輸出 -

['neg', 'pos']

現在看一下帶有正面評論的檔案的內容。這個檔案中的句子是標記化的，列印前四個句子來檢視樣本。

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])

當執行上面的程式時，我們得到以下輸出 -

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 .

接下來，通過使用nltk中的FreqDist函式來標記每個檔案中的單詞並找到最常用的單詞。

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))

當執行上面的程式時，我們得到以下輸出 -

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