应用词袋

Question

嘿，我正在处理词袋，我正在尝试实现，所以假设我有下面的语料库，但我不想使用 print( vectorizer.fit_transform(corpus).todense() ) 作为词汇表，而是我创建了一个像 {u'all': 0, u'sunshine': 1, u'some': 2, u'down': 3, u'reason': 4} 我如何使用这个词汇表来生成矩阵？

from sklearn.feature_extraction.text import CountVectorizer

corpus = [
'All my cats in a row',
'When my cat sits down, she looks like a Furby toy!',
'The cat from outer space',
'Sunshine loves to sit like this for some reason.'
]

vectorizer = CountVectorizer()
print( vectorizer.fit_transform(corpus).todense() )
print( vectorizer.vocabulary_ )

Answer 1

使用您的自定义词汇表实例化您的 CountVectorizer，然后转换您的语料库。

from sklearn.feature_extraction.text import CountVectorizer

corpus = [
'All my cats in a row',
'When my cat sits down, she looks like a Furby toy!',
'The cat from outer space',
'Sunshine loves to sit like this for some reason.'
]

vocabulary = {u'all': 0, u'sunshine': 1, u'some': 2, u'down': 3, u'reason': 4}

vectorizer = CountVectorizer(vocabulary=vocabulary)

print( vectorizer.transform(corpus).todense() )
[[1 0 0 0 0]
 [0 0 0 1 0]
 [0 0 0 0 0]
 [0 1 1 0 1]]

print( vectorizer.vocabulary_ )
{'all': 0, 'sunshine': 1, 'some': 2, 'down': 3, 'reason': 4}