在NLP中使用tf idf如何从python中的语料库（包含大量文档）中查找特定单词的频率

如何使用Tf idf从语料库中查找单个单词的频率。下面是我的示例代码，现在我想打印一个单词的频率。我怎样才能做到这一点

from sklearn.feature_extraction.text import CountVectorizer

vectorizer = CountVectorizer()
corpus = ['This is the first document.',
      'This is the second second document.',
      'And the third one.',
      'Is this the first document?',]
X = vectorizer.fit_transform(corpus)
X
print(vectorizer.get_feature_names())
X.toarray()
vectorizer.vocabulary_.get('document')

print(vectorizer.get_feature_names())

X.toarray()

vectorizer.vocabulary_.get('document')

---

您的

矢量化程序。词汇表\uu

具有每个单词的计数：

print(vectorizer.volcabulary_)

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

因此，计算词频很简单：

vocab = vectorizer.vocabulary_
tot = sum(vocab.values())
frequency = {vocab[w]/tot for w in vocab.keys()}

---

您的

矢量化程序。词汇表\uu

具有每个单词的计数：

print(vectorizer.volcabulary_)

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

因此，计算词频很简单：

vocab = vectorizer.vocabulary_
tot = sum(vocab.values())
frequency = {vocab[w]/tot for w in vocab.keys()}