[转载自]https://blog.csdn.net/weixin_43930694/article/details/98334465
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需求：

• 循环抓取豆瓣影评中所有观众对《陈情令》的评论，存储在文本文档中，并运用可视化库--词云对其进行分析
• 目标网站：https://movie.douban.com/subject/27195020/comments?start=
• 涉及的python类库：requests、lxml、wordcloud、numpy、PIL、jieba，可能还要安装matplotlib这个库
• 使用 Python 3.7

代码如下：

#-*- coding:utf-8 -*-
import requests
from lxml import etree
from wordcloud import WordCloud
import PIL.Image as image
import numpy as np
import jieba


# 获取html源代码
def getPage(url):
    headers = {
        "User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.142 Safari/537.36",
    }
    response = requests.get(url, headers=headers).text
    return response


# 获得所有页面
def all_page():
    base_url = "https://movie.douban.com/subject/27195020/comments?start="
    # 列表存放所有的网页，共10页
    urllist = []
    for page in range(0, 200, 20):
        allurl = base_url + str(page)
        urllist.append(allurl)
    return urllist


# 解析网页
def parse():
    # 列表存放所有的短评
    all_comment = []
    number = 1
    for url in all_page():
        # 初始化
        html = etree.HTML(getPage(url))
        # 短评
        comment = html.xpath('//div[@class="comment"]//p/span/text()')
        all_comment.append(comment)
        print('第' + str(number) + '页解析并保存成功')
        number += 1
    return all_comment


# 保存为txt
def save_to_txt():
    result = parse()
    for i in range(len(result)):
        with open('陈情令评论集.txt', 'a+', encoding='utf-8') as f:
            # 按行存储每一页数据
            f.write(str(result[i]) + '\n')
            f.close()


# 将爬取的文档进行分词
def trans_CN(text):
    word_list = jieba.cut(text)
    # 分词后在单独个体之间加上空格
    result = ' '.join(word_list)
    return result


# 制作词云
def getWordCloud():
    path_text = "陈情令评论集.txt"
    path_jpg = "timg.jpg"  # 当前目录下放一张这个图片
    path_font = "/Users/XXXX/project/simsun.ttf"  # 词云字体文件需要去网上找一个
    text = open(path_text, encoding='utf-8').read()
    # 剔除无关字
    text = text.replace("真的", " ")
    text = text.replace("什么", " ")
    text = text.replace("但是", " ")
    text = text.replace("而且", " ")
    text = text.replace("那么", " ")
    text = text.replace("就是", " ")
    text = text.replace("可以", " ")
    text = text.replace("不是", " ")

    text = trans_CN(text)
    mask = np.array(image.open(path_jpg))  # 词云背景图：事先要准备好一张图片
    wordcloud = WordCloud(
        background_color='white',
        mask=mask,
        scale=15,
        max_font_size=80,
        font_path=path_font
    ).generate(text)
    wordcloud.to_file('陈情令评论集.jpg')


# 主函数
if __name__ == '__main__':
    save_to_txt()
    print('所有页面保存成功')
    getWordCloud()
    print('词云制作成功')

搞定。这样就生成了一张词云的图片，就是出现频率越高的词，在这个词云图片中字体越大。如下：

陈情令评论集.jpg

最后附上字体simsun.ttf
链接:https://pan.baidu.com/s/1nTtwgyf7dCuJXUrWcfxs9A 密码:r025