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idf.py
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27 lines (23 loc) · 1.05 KB
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# -*- coding: utf-8 -*-
# Author: XuMing <[email protected]>
# Brief:
from sklearn.feature_extraction.text import TfidfVectorizer
corpus = ["I come to China to travel",
"This is a car polupar in China",
"I love tea and Apple ",
"The work is to write some papers in science"]
vectorizer = TfidfVectorizer()
tfidf = vectorizer.fit_transform(corpus)
print(tfidf)
print('vocab:')
print(vectorizer.vocabulary_)
word = vectorizer.get_feature_names() # 获取词袋模型中的所有词语
weight = tfidf.toarray() # 将tf-idf矩阵抽取出来，元素a[i][j]表示j词在i类文本中的tf-idf权重
for i in range(len(weight)): # 打印每类文本的tf-idf词语权重，第一个for遍历所有文本，第二个for便利某一类文本下的词语权重
print(u"-------这里输出第", i, u"类文本的词语tf-idf权重------")
k_v = dict()
for j in range(len(word)):
print(word[j], weight[i][j])
k_v[word[j]] = weight[i][j]
sorts = sorted(k_v.items(), key=lambda d:d[1],reverse=True)
print(sorts[:5])