使用scrapy爬取dota2贴吧数据并进行分析

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

# -*- 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()

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()

BOT_NAME = 'btspider'
SPIDER_MODULES = ['btspider.spiders']
NEWSPIDER_MODULE = 'btspider.spiders'
ROBOTSTXT_OBEY = True
ITEM_PIPELINES = {
   'btspider.pipelines.BtspiderPipeline': 300,
}

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()

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