使用sklearn提取文本的tfidf特征

最新推荐文章于 2025-07-02 15:22:19 发布

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最新推荐文章于 2025-07-02 15:22:19 发布 · 5.9k 阅读

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#文本特征提取 #TF_IDF #NLP

from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer

corpus = [
    'This is the first document.',
    'This is the second second document.',
    'And the third one.',
    'Is this the first document?',
]

CountVectorizer是通过fit_transform函数将文本中的词语转换为词频矩阵
get_feature_names()可看到所有文本的关键字
vocabulary_可看到所有文本的关键字和其位置
toarray()可看到词频矩阵的结果

vectorizer = CountVectorizer()
count = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())  
print(vectorizer.vocabulary_)
print(count.toarray())

['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']
{'this': 8, 'is': 3, 'the': 6, 'first': 2, 'document': 1, 'second': 5, 'and': 0, 'third': 7, 'one': 4}
[[0 1 1 1 0 0 1 0 1]
 [0 1 0 1 0 2 1 0 1]
 [1 0 0 0 1 0 1 1 0]
 [0 1 1 1 0 0 1 0 1]]

TfidfTransformer是统计CountVectorizer中每个词语的tf-idf权值