一直好奇贴吧里的小伙伴们在过去的时间里说的最多的词是什么，那我们就来抓取分析一下贴吧发文的标题内容，并提取分析一下，看看吧友们在说些什么。
首先我们使用scrapy对所有贴吧文章的标题进行抓取
scrapy startproject btspider
cd btspider
scrapy genspider -t basic btspiderx tieba.baidu.com
修改btspiderx内容
# -*- coding: utf-8 -*-
import scrapy
from btspider.items import BtspiderItem
class BTSpider(scrapy.Spider):
name = "btspider"
allowed_domains = ["baidu.com"]
start_urls = []
for x in xrange(91320):
if x == 0:
url = "https://tieba.baidu.com/f?kw=dota2&ie=utf-8"
else:
url = "https://tieba.baidu.com/f?kw=dota2&ie=utf-8&pn=" + str(x*50)
start_urls.append(url)
def parse(self, response):
for sel in response.xpath('//div[@class="col2_right j_threadlist_li_right "]'):
item = BtspiderItem()
item['title'] = sel.xpath('div/div/a/text()').extract()
item['link'] = sel.xpath('div/div/a/@href').extract()
item['time'] = sel.xpath(
'div/div/span[@class="threadlist_reply_date pull_right j_reply_data"]/text()').extract()
yield item修改items.py
# -*- coding: utf-8 -*-
# Define here the models for your scraped items
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/items.html
import scrapy
class BtspiderItem(scrapy.Item):
title = scrapy.Field()
link = scrapy.Field()
time = scrapy.Field()这里我们实际上保存的只是title标题内容
修改pipelines.py
# -*- coding: utf-8 -*-
# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: https://doc.scrapy.org/en/latest/topics/item-pipeline.html
import codecs
import json
class BtspiderPipeline(object):
def __init__(self):
self.file = codecs.open('info', 'w', encoding='utf-8')
def process_item(self, item, spider):
# line = json.dumps(dict(item)) + "\n"
titlex = dict(item)["title"]
if len(titlex) != 0:
title = titlex[0]
#linkx = dict(item)["link"]
#if len(linkx) != 0:
# link = 'http://tieba.baidu.com' + linkx[0]
#timex = dict(item)["time"]
#if len(timex) != 0:
# time = timex[0].strip()
line = title + '\n' #+ link + '\n' + time + '\n'
self.file.write(line)
return item
def spider_closed(self, spider):
self.file.close()修改settings.py
BOT_NAME = 'btspider'
SPIDER_MODULES = ['btspider.spiders']
NEWSPIDER_MODULE = 'btspider.spiders'
ROBOTSTXT_OBEY = True
ITEM_PIPELINES = {
'btspider.pipelines.BtspiderPipeline': 300,
}启动爬虫
scrapy crawl btspider
所有的标题内容会被保存为info文件
等到爬虫结束，我们来分析info文件的内容
github上有个示例，改改就能用
git clone https://github.com/FantasRu/WordCloud.git
修改main.py文件如下：
# coding: utf-8
from os import path
import numpy as np
# import matplotlib.pyplot as plt
# matplotlib.use('qt4agg')
from wordcloud import WordCloud, STOPWORDS
import jieba
class WordCloud_CN:
'''
use package wordcloud and jieba
generating wordcloud for chinese character
'''
def __init__(self, stopwords_file):
self.stopwords_file = stopwords_file
self.text_file = text_file
@property
def get_stopwords(self):
self.stopwords = {}
f = open(self.stopwords_file, 'r')
line = f.readline().rstrip()
while line:
self.stopwords.setdefault(line, 0)
self.stopwords[line.decode('utf-8')] = 1
line = f.readline().rstrip()
f.close()
return self.stopwords
@property
def seg_text(self):
with open(self.text_file) as f:
text = f.readlines()
text = r' '.join(text)
seg_generator = jieba.cut(text)
self.seg_list = [
i for i in seg_generator if i not in self.get_stopwords]
self.seg_list = [i for i in self.seg_list if i != u' ']
self.seg_list = r' '.join(self.seg_list)
return self.seg_list
def show(self):
# wordcloud = WordCloud(max_font_size=40, relative_scaling=.5)
wordcloud = WordCloud(font_path=u'./static/simheittf/simhei.ttf',
background_color="black", margin=5, width=1800, height=800)
wordcloud = wordcloud.generate(self.seg_text)
# plt.figure()
# plt.imshow(wordcloud)
# plt.axis("off")
# plt.show()
wordcloud.to_file("./demo/" + self.text_file.split('/')[-1] + '.jpg')
if __name__ == '__main__':
stopwords_file = u'./static/stopwords.txt'
text_file = u'./demo/info'
generater = WordCloud_CN(stopwords_file)
generater.show()然后启动分析
python main.py
由于数据比较大，分析时间会比较长，可以拿到廉价的单核云主机上后台分析，等着那结果就好。
下边是我分析两个热门游戏贴吧的词云图片
