python爬虫 2019中国好声音评论爬取过程解析
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:
github地址:https://github.com/FJCAAAAA/python-spider
注:本文章只用于学习使用
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