tf-idf中文文本分类预处理的python实现，返回值为tf-idf值

好的，下面是tf-idf中文文本分类预处理的Python实现，返回值为tf-idf值：

import math
import jieba
from collections import Counter

def preprocess(documents):
    corpus = []
    for document in documents:
        words = jieba.cut(document)
        corpus.append(words)
    return corpus

def tf(word, words):
    freq = words.count(word)
    return freq / len(words)

def idf(word, corpus):
    num_documents_containing_word = sum(1 for document in corpus if word in document)
    return math.log(len(corpus) / num_documents_containing_word)

def tf_idf(word, words, corpus):
    return tf(word, words) * idf(word, corpus)

def get_tfidf(corpus):
    tfidf = []
    for words in corpus:
        document_tfidf = {}
        word_counts = Counter(words)
        for word, count in word_counts.items():
            document_tfidf[word] = tf_idf(word, words, corpus)
        tfidf.append(document_tfidf)
    return tfidf

其中，preprocess函数用于对中文文本进行分词处理，tf函数和idf函数与之前提到的一样，get_tfidf函数用于计算整个文档集合的tf-idf值。使用时，需要传入文档集合，返回值为一个列表，其中每个元素为一个字典，表示对应文档中每个单词的tf-idf值。