必做题：

1. 数据准备：academy_titles.txt为“考硕考博”板块的帖子标题，job_titles.txt为“招聘信息”板块的帖子标题，
2. 使用jieba工具对academy_titles.txt进行分词，接着去除停用词，然后统计词频，最后绘制词云。同样的，也绘制job_titles.txt的词云。
3. 将jieba替换为pkuseg工具，分别绘制academy_titles.txt和job_titles.txt的词云。要给出每一部分的代码。

效果图

 

代码

import jieba
import re
from wordcloud import WordCloud
from collections import Counter
import matplotlib.pyplot as plt

# 读取academy_titles文件内容
with open('C:\\Users\\hp\\Desktop\\实验3\\academy_titles.txt', 'r', encoding='utf-8') as file:
    academy_titles = file.readlines()

# 读取job_titles文件内容
with open('C:\\Users\\hp\\Desktop\\实验3\\job_titles.txt', 'r', encoding='utf-8') as file:
    job_titles = file.readlines()

# 将招聘信息与学术信息分开
academy_titles = [title.strip() for title in academy_titles]
job_titles = [title.strip() for title in job_titles]

# 分词、去除停用词、统计词频（对academy_titles）
academy_words = []
for title in academy_titles:
    words = jieba.cut(title)
    filtered_words = [word for word in words if re.match(r'^[\u4e00-\u9fa5]+$', word)]
    academy_words.extend(filtered_words)

请自行补全代码，或者这周五晚上更新完整代码