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8-2 【Python0026】图书评论数据分析与可视化

分数 10

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作者 doublebest

单位 石家庄铁道大学

【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。

 

 

import re

from collections import Counter

import requests

# from lxml import etree

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"

"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.0.0 Safari/537.36"

}

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 = \[\]

word s =\[\]

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)