增补博客 第九篇 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。

 
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)

  

热门相关:来自地狱的男人   秦吏   妖魔哪里走   黄金渔场   这个赘婿有点强