python-图书评论数据分析与可视化

【题目描述】豆瓣图书评论数据爬取。以《平凡的世界》、《都挺好》等为分析对象,编写程序爬取豆瓣读书上针对该图书的短评信息,要求:

(1)对前3页短评信息进行跨页连续爬取;

(2)爬取的数据包含用户名、短评内容、评论时间、评分和点赞数(有用数);

(3)能够根据选择的排序方式(热门或最新)进行爬取,并分别针对热门和最新排序,输出前10位短评信息(包括用户名、短评内容、评论时间、评分和点赞数)。

(4)根据点赞数的多少,按照从多到少的顺序将排名前10位的短评信息输出;

(5附加)结合中文分词和词云生成,对前3页的短评内容进行文本分析:按照词语出现的次数从高到低排序,输出前10位排序结果;并生成一个属于自己的词云图形。

【练习要求】请给出源代码程序和运行测试结果,源代码程序要求添加必要的注释。

源代码:

import re from collections import Counter  import requests from lxml import etree import pandas as pd import jieba import matplotlib.pyplot as plt from wordcloud import WordCloud  headers = {     User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.54 Safari/537.36 Edg/101.0.1210.39 }  comments = [] words = []   def regex_change(line):     # 前缀的正则     username_regex = re.compile(r^\d+::)     # URL,为了防止对中文的过滤,所以使用[a-zA-Z0-9]而不是\w     url_regex = re.compile(r         (https?://)?         ([a-zA-Z0-9]+)         (\.[a-zA-Z0-9]+)         (\.[a-zA-Z0-9]+)*         (/[a-zA-Z0-9]+)*     , re.VERBOSE | re.IGNORECASE)     # 剔除日期     data_regex = re.compile(u        #utf-8编码         年 |         月 |         日 |         (周一) |         (周二) |          (周三) |          (周四) |          (周五) |          (周六)     , re.VERBOSE)     # 剔除所有数字     decimal_regex = re.compile(r[^a-zA-Z]\d+)     # 剔除空格     space_regex = re.compile(r\s+)     regEx = [\n”“|,,;;''/?! 。的了是]  # 去除字符串中的换行符、中文冒号、|,需要去除什么字符就在里面写什么字符     line = re.sub(regEx, , line)     line = username_regex.sub(r, line)     line = url_regex.sub(r, line)     line = data_regex.sub(r, line)     line = decimal_regex.sub(r, line)     line = space_regex.sub(r, line)     return line   def getComments(url):     score = 0     resp = requests.get(url, headers=headers).text     html = etree.HTML(resp)     comment_list = html.xpath(.//div[@class='comment'])     for comment in comment_list:         status =          name = comment.xpath(.//span[@class='comment-info']/a/text())[0]  # 用户名         content = comment.xpath(.//p[@class='comment-content']/span[@class='short']/text())[0]  # 短评内容         content = str(content).strip()         word = jieba.cut(content, cut_all=False, HMM=False)         time = comment.xpath(.//span[@class='comment-info']/a/text())[1]  # 评论时间         mark = comment.xpath(.//span[@class='comment-info']/span/@title)  # 评分         if len(mark) == 0:             score = 0         else:             for i in mark:                 status = str(i)             if status == 力荐:                 score = 5             elif status == 推荐:                 score = 4             elif status == 还行:                 score = 3             elif status == 较差:                 score = 2             elif status == 很差:                 score = 1         good = comment.xpath(.//span[@class='comment-vote']/span[@class='vote-count']/text())[0]  # 点赞数(有用数)         comments.append([str(name), content, str(time), score, int(good)])         for i in word:             if len(regex_change(i)) >= 2:                 words.append(regex_change(i))   def getWordCloud(words):     # 生成词云     all_words = []     all_words += [word for word in words]     dict_words = dict(Counter(all_words))     bow_words = sorted(dict_words.items(), key=lambda d: d[1], reverse=True)     print(热词前10位:)     for i in range(10):         print(bow_words[i])     text = ' '.join(words)      w = WordCloud(background_color='white',                      width=1000,                      height=700,                      font_path='simhei.ttf',                      margin=10).generate(text)     plt.show()     plt.imshow(w)     w.to_file('wordcloud.png')   print(请选择以下选项:) print(   1.热门评论) print(   2.最新评论) info = int(input()) print(前10位短评信息:) title = ['用户名', '短评内容', '评论时间', '评分', '点赞数'] if info == 1:     comments = []     words = []     for i in range(0, 60, 20):         url = https://book.douban.com/subject/10517238/comments/?start={}&limit=20&status=P&sort=new_score.format(             i)  # 前3页短评信息(热门)         getComments(url)     df = pd.DataFrame(comments, columns=title)     print(df.head(10))     print(点赞数前10位的短评信息:)     df = df.sort_values(by='点赞数', ascending=False)     print(df.head(10))     getWordCloud(words) elif info == 2:     comments = []     words=[]     for i in range(0, 60, 20):         url = https://book.douban.com/subject/10517238/comments/?start={}&limit=20&status=P&sort=time.format(             i)  # 前3页短评信息(最新)         getComments(url)     df = pd.DataFrame(comments, columns=title)     print(df.head(10))     print(点赞数前10位的短评信息:)     df = df.sort_values(by='点赞数', ascending=False)     print(df.head(10))     getWordCloud(words)

运行结果: