python爬虫 2019中国好声音评论爬取过程解析

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2019中国好声音火热开播，作为一名“假粉丝”，这一季每一期都刷过了，尤其刚播出的第六期开始正式的battle。视频视频看完了，那看下大家都是怎样评论的。

1.网页分析部分

本文爬取的是腾讯视频评论，第六期的评论地址是：http://coral.qq.com/4093121984

每页有10条评论，点击“查看更多评论”，可将新的评论加载进来，通过多次加载，可以发现我们要找的评论就在以v2开头的js类型的响应中。

请求为GET请求，地址是http://coral.qq.com/article/4093121984/comment/v2 ，通过传入不同的参数返回不同的评论内容。

经过对比发现，参数不同的地方只有两点，"cursor"和""。

先看"cursor"：第一页的"cursor"是0，后面每一页的都是前一页响应中"last"的值

再看下""：第一页的值似乎是随机生成的，而后面每一页都在前一页的基础上加1

OK，找到规律后，开始爬取每一页的评论

2.爬虫部分

（1）导入需要的库

import requests
import re
import random
import time
import json
import jieba
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib.font_manager as fmgr
from wordcloud import WordCloud
from common import user_agent #自定义
from common import my_fanction #自定义

其中common文件夹中自定义了一些方法：

user_agent

#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
@File : user_agent.py
@Author: Fengjicheng
@Date : 2019/8/11
@Desc :
'''
user_agent_list = [
    # Opera
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36 OPR/26.0.1656.60",
    "Opera/8.0 (Windows NT 5.1; U; en)",
    "Mozilla/5.0 (Windows NT 5.1; U; en; rv:1.8.1) Gecko/20061208 Firefox/2.0.0 Opera 9.50",
    "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; en) Opera 9.50",
    # Firefox
    "Mozilla/5.0 (Windows NT 6.1; WOW64; rv:34.0) Gecko/20100101 Firefox/34.0",
    "Mozilla/5.0 (X11; U; Linux x86_64; zh-CN; rv:1.9.2.10) Gecko/20100922 Ubuntu/10.10 (maverick) Firefox/3.6.10",
    # Safari
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/534.57.2 (KHTML, like Gecko) Version/5.1.7 Safari/534.57.2",
    # chrome
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.71 Safari/537.36",
    "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11",
    "Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US) AppleWebKit/534.16 (KHTML, like Gecko) Chrome/10.0.648.133 Safari/534.16",
    # 360
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/30.0.1599.101 Safari/537.36",
    "Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; rv:11.0) like Gecko",
    # 淘宝浏览器
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.11 TaoBrowser/2.0 Safari/536.11",
    # 猎豹浏览器
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.71 Safari/537.1 LBBROWSER",
    "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E; LBBROWSER)",
    "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E; LBBROWSER)",
    # QQ浏览器
    "Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0; SLCC2; .NET CLR 2.0.50727; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; .NET4.0C; .NET4.0E; QQBrowser/7.0.3698.400)",
    "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; QQDownload 732; .NET4.0C; .NET4.0E)",
    # sogou浏览器
    "Mozilla/5.0 (Windows NT 5.1) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.84 Safari/535.11 SE 2.X MetaSr 1.0",
    "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Trident/4.0; SV1; QQDownload 732; .NET4.0C; .NET4.0E; SE 2.X MetaSr 1.0)",
    # maxthon浏览器
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Maxthon/4.4.3.4000 Chrome/30.0.1599.101 Safari/537.36",
    # UC浏览器
    "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/38.0.2125.122 UBrowser/4.0.3214.0 Safari/537.36",
  ]

my_function

#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
@File : file_writte.py
@Author: Fengjicheng
@Date : 2019/8/24
@Desc :
'''
def file_write(file_name,content):
  if content:
    if type(content) == list:
      for i in content:
        with open(file_name,'a',encoding='utf-8') as f:
          f.write(i + '\n')
    if type(content) == str:
      with open(file_name, 'a', encoding='utf-8') as f:
        f.write(content)
  else:
    print(content,"内容为空，跳过")
    pass

（2）爬取评论内容

这里总共爬取了三种类型的数据：用户评论、用户昵称、用户所在地区

#评论请求地址
url = 'http://coral.qq.com/article/4093121984/comment/v2'
agent = random.choice(user_agent.user_agent_list)
header = {
'Host': 'video.coral.qq.com',
'User-Agent': agent,
'Accept': '*/*',
'Accept-Language': 'zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2',
'Accept-Encoding': 'gzip, deflate, br',
'Connection': 'keep-alive',
'Referer': 'https://page.coral.qq.com/coralpage/comment/video.html',
'TE': 'Trailers'
}
# 第一页
cursor = '0'
vid = 1566724116229

def get_comment(a,b):
  parameter = {
  'callback': '_varticle4093121984commentv2',
  'orinum': '10',
  'oriorder': 'o',
  'pageflag': '1',
  'cursor': a,
  'scorecursor': '0',
  'orirepnum': '2',
  'reporder': 'o',
  'reppageflag': '1',
  'source': '1',
  '_': str(b)
  }
  try:
    html = requests.get(url,params=parameter,headers=header)
  except Exception as e:
    print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),"请求失败。",e)
  else:
    print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),"请求成功。")
  content = html.content.decode('utf-8')
  sep1 = '"last":"(.*"' # 下一个 cursor
  sep2 = '"content":"(.*"' # 评论
  sep3 = '"nick":"(.*"' # 昵称
  sep4 = '"region":"(.*"' # 地区
  global cursor
  cursor = re.compile(sep1).findall(content)[0]
  comment = re.compile(sep2).findall(content)
  nick = re.compile(sep3).findall(content)
  region = re.compile(sep4).findall(content)
  my_fanction.file_write('txt/comment.txt',comment)
  my_fanction.file_write('txt/nick.txt',nick)
  my_fanction.file_write('txt/region.txt',region)

效果如下：

（3）对用户评论进行分词

def cut_word(file_path):
  with open(file_path,'r',encoding='utf-8') as f:
    comment_txt = f.read()
    wordlist = jieba.cut(comment_txt, cut_all=True)
    wl = " ".join(wordlist)
    print(wl)
    return wl #返回分词后的数据

（4）生成词云

#词云形状图片
img1 = 'lib/fangxing.png'
img2 = 'lib/xin.png'
#词云字体
font = 'lib/simsun.ttc'
def create_word_cloud(file_path,img):
  # 设置词云形状图片
  wc_mask = np.array(Image.open(img))
  # 设置词云的一些配置，如：字体，背景色，词云形状，大小
  wc = WordCloud(background_color="white", max_words=200, mask=wc_mask, scale=4,
          max_font_size=50, random_state=42, font_path=font)
  # 生成词云
  wc.generate(cut_word(file_path))
  # 在只设置mask的情况下,你将会得到一个拥有图片形状的词云
  plt.imshow(wc, interpolation="bilinear")
  plt.axis("off")
  #plt.figure()
  plt.show()

效果如下：

（5）对用户地区统计分析

国外地区忽略了，这里只对国内地区进行了分析

def create_region_histogram():
  with open('txt/region.txt','r',encoding='utf-8') as f:
    country_list = f.readlines()
    country_list = [x.strip() for x in country_list if x.strip() != '::']
  sep1 = ':'
  pattern1 = re.compile(sep1)
  province_lit = []
  province_count = []
  other_list = []
  other_count = []
  for country in country_list:
    country_detail = re.split(pattern1,country)
    if '中国' in country_detail:
      if country_detail[1] != '':
        province_lit.append(country_detail[1])
    else:
      other_list.append(country_detail[0])
  province_uniq = list(set(province_lit))
  other_uniq = list(set(other_list))
  for i in province_uniq:
    province_count.append(province_lit.count(i))
  for i in other_uniq:
    other_count.append(other_list.count(i))
  # 构建数据
  x_data = province_uniq
  y_data = province_count
  # 自定义字体属性
  fp = fmgr.FontProperties(fname='lib/simsun.ttc')
  bar_width = 0.7
  # Y轴数据使用range(len(x_data)
  plt.barh(y=range(len(x_data)), width=y_data, label='count',
       color='steelblue', alpha=0.8, height=bar_width)
  # 在柱状图上显示具体数值, ha参数控制水平对齐方式, va控制垂直对齐方式
  for y, x in enumerate(y_data):
    plt.text(x+10, y - bar_width / 2, '%s' % x, ha='center', va='bottom')
  # 为Y轴设置刻度值
  plt.yticks(np.arange(len(x_data)) + bar_width / 2, x_data,fontproperties=fp)
  # 设置标题
  plt.title("各地区参与评论用户量",fontproperties=fp)
  # 为两条坐标轴设置名称
  plt.xlabel("人数",fontproperties=fp)
  plt.ylabel("地区",fontproperties=fp)
  # 显示图例
  plt.legend()
  plt.show()

效果如下：