It has been several days since "later we" was released. I haven't been to see it. There was a ticket refund incident a few days ago. The theme song of the movie was sung by Eason Chan. I specially looked for the MV of the theme song "we" and watched it. It's still that feeling. That day, I saw an official account of the Python Chinese community public. New discovery of 100000 comments on Eason Chan's new song "we" in Python . Recently, I have been learning Python, trying to find an interesting project to do an exercise, so I started to practice by imitating the author's code. In the original, the author said in the title "new discovery of 100000 comments". Through the operation of the program, I found that the author didn't crawl all comments, just crawled the popular comments of "we" in Netease cloud music, and according to the number According to the chart.

Netease cloud "we"

Code:

 1 #!/usr/bin/env python3
 2 # -*- coding: utf-8 -*-
 3 # @Time    : 2018/4/29 18:09
 4 # @Author  : yang
 5 # @File    : Code.py
 6 # @Software: PyCharm
 7 import requests
 8 import json
 9 
10 #Crawling through the popular comments of Eason Chan's "we"
11 #Parameters: url,headers,user_data(params,encSecKey)
12 url = 'http://music.163.com/weapi/v1/resource/comments/R_SO_4_551816010?csrf_token='    #Link to comment
13 headers = {
14     'User-Agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36',
15     'Referer':'http://music.163.com/song?id=551816010',
16     'Origin':'http://music.163.com',
17     'Host':'music.163.com'
18 }
19 #Encrypt the data and use it directly
20 user_data = {
21     'params':'60e75d03+rb9U8IQhy6/9+H1si5pp7qLysZoQsYG9qFkXtXL9dRKMfchCKpJ8OpN9m7vSRVkYWN+wscyUqelunqxGDozt2bJWQ2QRj4pJrSa0xoJPAk5Jw8t70rYW8hwdyoYswl+kRQTQ6oz3eHHZ5BLzZZB4t/4asFSQQDnCteg2GqrEJBomMgpFMIa4Ybt',
22     'encSecKey':'52db8824c86503bc2cfc050ac78969c9155ff08f274f88b767ad6535febcbad021d0cdabcc172e01f91c42a2aca0786e407935f8feaa44a03efb96ec9d71de181e92ae8471738e4a43b252f22b46739cb3b86544a9f9403b0402bd9638a3bc2b87bf3a0b9cff6ef7b6b1589f00a5bfeecb9d45c493456082d80fbece6ac5a3fa'
23 }
24 
25 response = requests.post(url,headers=headers,data=user_data)
26 data = json.loads(response.text)
27 hotcomments = []
28 for hotcomment in data['hotComments']:
29     item = {
30         'nickname':hotcomment['user']['nickname'],
31         'content':hotcomment['content'],
32         'likedCount':hotcomment['likedCount']
33     }
34     hotcomments.append(item)
35 #Get comment user name, content, and the corresponding number of likes
36 content_list = [content['content'] for content in hotcomments]
37 nickname = [content['nickname'] for content in hotcomments]
38 liked_count = [content['likedCount'] for content in hotcomments]
39 
40 #Praise points
41 from pyecharts import Bar      #pyecharts: Charting package
42 bar = Bar('Example of popular likes')
43 bar.add('Praise points',nickname,liked_count,is_stack=True,mark_line=['min','max'],mark_point=['average'])
44 bar.render()
45 
46 #Word cloud diagram
47 from wordcloud import WordCloud     #WordCloud: Word cloud package
48 import matplotlib.pyplot as plt     #matplotlib: Drawing function package
49 content_text = ' '.join(content_list)
50 wordcloud = WordCloud(font_path=r'C:\simhei.ttf',max_words=200).generate(content_text)
51 plt.figure()
52 plt.imshow(wordcloud,interpolation='bilinear')
53 plt.axis('off')
54 plt.show()

Crawling results:

Top comments like:

Cloud of popular comments: