如何在 Python 中使用 nltk.corpus 逐行读取和标记文本文件

Question

我的问题是给定两个训练数据good_reviews.txt 和bad_reviews.txt 对文档进行分类。因此，首先我需要加载和标记我的训练数据，其中每一行都是对应于评论的文档本身。所以我的主要任务是从给定的测试数据中对评论（行）进行分类。

我找到了一种加载和标记名称数据的方法，如下所示：

from nltk.corpus import names
names = ([(name, 'male') for name in names.words('male.txt')] +
         [(name, 'female') for name in names.words('female.txt')])

所以我想要的是一个类似的东西，标签lines 而不是words。 我希望代码是这样的，因为.lines 是无效的语法，所以这当然不起作用：

reviews = ([(review, 'good_review') for review in reviews.lines('good_reviews.txt')] +
           [(review, 'bad_review') for review in reviews.lines('bad_reviews.txt')])

我想要这样的结果：

>>> reviews[0]
('This shampoo is very good blablabla...', 'good_review')

Answer 1

如果您正在阅读自己的文本文件，那么与NLTK 没什么关系，您可以简单地使用file.readlines()：

good_reviews = """This is great!
Wow, it amazes me...
An hour of show, a lifetime of enlightment
"""
bad_reviews = """Comme si, Comme sa.
I just wasted my foo bar on this.
An hour of s**t, ****.
"""
with open('/tmp/good_reviews.txt', 'w') as fout:
    fout.write(good_reviews)
with open('/tmp/bad_reviews.txt', 'w') as fout:
    fout.write(bad_reviews)

reviews = []
with open('/tmp/good_reviews.txt', 'r') as fingood, open('/tmp/bad_reviews.txt', 'r') as finbad:
    reviews = ([(review, 'good_review') for review in fingood.readlines()] + [(review, 'bad_review') for review in finbad.readlines()])

print reviews

[出]：

[('This is great!\n', 'good_review'), ('Wow, it amazes me...\n', 'good_review'), ('An hour of show, a lifetime of enlightment\n', 'good_review'), ('Comme si, Comme sa.\n', 'bad_review'), ('I just wasted my foo bar on this.\n', 'bad_review'), ('An hour of s**t, ****.\n', 'bad_review')]

如果您要使用 NLTK 电影评论语料库，请参阅 Classification using movie review corpus in NLTK/Python