1.选一个自己感兴趣的主题。

2.网络上爬取相关的数据。

3.进行文本分析,生成词云。

4.对文本分析结果解释说明。

5.写一篇完整的博客,附上源代码、数据爬取及分析结果,形成一个可展示的成果。

 

我选择主题是游戏资讯,爬取的网站是:http://www.gamersky.com/news/

 

 爬取此网页中一些游戏新闻标题、发布时间、来源以及地址:

import requests
from bs4 import BeautifulSoup
from datetime import datetime
import re

url = 'http://www.gamersky.com/news/'
res = requests.get(url)
res.encoding='utf-8'   
soup=BeautifulSoup(res.text,'html.parser')

for news in soup.select('.Mid2L_con'):
    for news2 in news.select('li'):
        if len(news2)>0:
            title=news2.select('.tt')[0]['title']
            url=news2.select('.tt')[0]['href']
            time=news2.select('.time')[0].text
            
            print('标题:',title)
            print('链接:',url)
            print('时间:',time)

 

效果如下图所示:

 

进行文本分析,生成词云:

import requests
import jieba
from bs4 import BeautifulSoup
import re

url = 'http://www.gamersky.com/news/'
res = requests.get(url)
res.encoding='utf-8'   
soup=BeautifulSoup(res.text,'html.parser')


for news in soup.select('.Mid2L_con'):
    for news2 in news.select('li'):
        if len(news2)>0:
            title=news2.select('.tt')[0]['title']
            url=news2.select('.tt')[0]['href']

            resd=requests.get(url)
            resd.encoding='utf-8'
            soupd=BeautifulSoup(resd.text,'html.parser')
            p = soupd.select('p')[0].text
            #print(p)
            break
            
words = jieba.lcut(p)
ls = []
counts = {}
for word in words:
    ls.append(word)
    if len(word) == 1:
        continue
    else:
        counts[word] = counts.get(word,0)+1
items = list(counts.items())
items.sort(key = lambda x:x[1], reverse = True)
for i in range(10):
    word , count = items[i]
    print ("{:<5}{:>2}".format(word,count))

from wordcloud import WordCloud
import matplotlib.pyplot as plt    
cy = WordCloud(font_path='msyh.ttc').generate(p)#wordcloud默认不支持中文,这里的font_path需要指向中文字体
plt.imshow(cy, interpolation='bilinear')
plt.axis("off")
plt.show()

 

效果如下图所示:

 

posted on 2017-11-02 10:23  18-刘卓辉  阅读(250)  评论(0编辑  收藏  举报