【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)