在使用jieba计算tf-idf时候，

jieba.analyse.extract_tags(txt, topK=10, withWeight=True, allowPOS=())

TF根据txt的输入计算，而每个词IDF是jieba内部给定的。

想根据已有的文本计算idf，利用jieba的分词，计算自定义idf语料库

输入

如图当作三篇文章，通过换行作为文章间隔，最后一篇也要换行

输出

全部代码

#自定义计算idf语料库 python全部代码
import jieba
import math
import jieba.analyse

txt = open(r"C:\Users\19624\Desktop\dissertation\Multiword\a.txt", "r", encoding='utf-8').read()
filename1 = r"C:\Users\19624\Desktop\dissertation\Multiword\idf.txt"
filename2 = r"C:\Users\19624\Desktop\dissertation\Multiword\wdic.txt"

words = jieba.lcut(txt)     # 使用精确模式对文本进行分词


with open(filename2, 'a+', encoding='utf-8') as file2:### 每次运行清空一下文件
     file2.truncate(0)

with open(filename2, 'a', encoding='utf-8') as file2:#### 将jieba分词结果写入，每词一行，以&&&为每个文本间隔
    for i in range(0, len(words)):
        if words[i] == '\n':
            file2.write('&&&'+'\n')  # 写入文件
        else:
            file2.write(words[i]+'\n')  # 写入文件

with open(filename2, encoding='utf-8') as file:
    contents = file.readlines()  # 读取文件
newList = []
for content in contents:
    newContent = content.replace('\n', '')
    newList.append(newContent)  # 存放在列表中

num = 0  ##统计总文档数
for i in range(0, len(newList)):
    if newList[i] == '&&&':
        num+=1

with open(filename1, 'a+', encoding='utf-8') as f:  ### 每次运行清空一下文件
    f.truncate(0)

###计算每个词的idf
for i in range(0, len(newList)):
    num1=0
    word=newList[i]
    j=0
    while j < len(newList):
        if word==newList[j]:
            num1+=1
            while newList[j]!='&&&':
                j+=1
        j+=1
    idf=math.log10(num/num1)
    with open(filename1, 'a', encoding='utf-8') as file1:
        file1.write(word + ' ' + str(idf) + '\n')  # 写入文件

换一下输入输出路径，应该就没问题。
txt是输入，filename1是最终输出。