【汽车口碑分析】3.爬取汽车评论数据项目

环境配置

Ubuntu 16.04
Python 3.5

技术框架

Scrapy

需求目标本项目为汽车口碑分析，第一步需要爬取对于不同车型的评论数据。
选择58车的车型分类爬取评论数据。
爬取流程

先获取每个车型的链接，以下图中红框内的车型为例
打开链接后，抓取下图红框中的总评分，写入文件中。
写入总评分后，通过拼接链接进入该车型的用户评论页面。
通过第一步中获取的链接拼接上list_s1_p1.html，组成用户评论页面的链接。
【注】此为第一页的链接，若还有下一页，下述步骤会提及处理方法。
抓取评论页面中的各种数据，如id，评分，评论等。
【【汽车口碑分析】3.爬取汽车评论数据】
若该评论页面还有下一页，则继续抓取下一页中的评论数据。
【方法】
判断页面中是否有下一页元素，若有则回调解析评论页面的方法。
将爬取的数据保存到文件中。

详细步骤创建新工程
先创建工程目录

cd /home/t/dataset/ mkdir carSpider

创建新工程

scrapy startproject carSpider

编辑items.py文件

# -*- coding: utf-8 -*-# Define here the models for your scraped items # # See documentation in: # http://doc.scrapy.org/en/latest/topics/items.htmlimport scrapy class CarspiderItem(scrapy.Item): file=scrapy.Field() #文件名 car=scrapy.Field() #车型 score=scrapy.Field() #总评分 u_id=scrapy.Field() #用户ID u_score=scrapy.Field() #用户评分 u_merit=scrapy.Field() #用户评论优点 u_demerit=scrapy.Field() #用户评论缺点 u_summary=scrapy.Field() #用户评论综述 u_flower=scrapy.Field() #用户评论鲜花数 u_brick=scrapy.Field() #用户评论板砖数

编写carSpider.py文件

import scrapy from carSpider.items import CarspiderItembaseDir = '/home/t/dataset/carRemark/' startUrl='http://www.58che.com/brand.html'class CarSpider(scrapy.Spider):name='spider' #爬虫名 def __init__(self): self.start_urls=[startUrl] #第一层解析方法 def parse(self,response): #定位到车型元素 subclasses=response.css('body > div.fltop > div.marcenter > div > div > div.r > ul > li > dl > dt > a') for subclass in subclasses: subclass_name=subclass.xpath('text()').extract_first() #获取车型名称文本 subclass_link=subclass.xpath('@href').extract_first() #获取车型链接 yield scrapy.Request(url=subclass_link,callback=self.parse_car_subclass,meta={'file':subclass_name}) #回调下一层解析方法，并把车型名称传递给该方法作为文件名#第二层解析方法 def parse_car_subclass(self,response): infos=response.css('#line1 > div.cars_line2.l > div.dianpings > div.d_div1.clearfix > font') #定位到总评分元素 for info in infos: score=info.xpath('text()').extract_first() #获取总评分元素文本 file=response.meta['file'] #获取上个Request传递来的meta['file'] self.writeScore(file,score) #将总评分写入文件中 link=response.url+'list_s1_p1.html' #拼接用户评论第一页链接 yield scrapy.Request(url=link,callback=self.parse_remark,meta={'file':file}) #回调下一层解析方法，把车型名称传递给该方法作为文件名#第三层解析方法 def parse_remark(self,response): #定位到用户评论元素 infos=response.css('body > div.newbox > div > div.xgo_cars_w760.l > div.xgo_dianping_infos.mb10 > div.xgo_cars_dianping > div > dl') for info in infos: uid=info.xpath('dd[1]/strong/a/text()')[0].extract() #获取用户ID score=info.xpath('dd[1]/div/div/@style')[0].extract() #获取用户评分星级 score=self.getScore(score) #将用户评分星级转化为5分制评分try: #先获取是否有‘优点’元素，若有则定位‘优点’元素的下一个兄弟节点，即‘优点评语’，若无则为空 node=info.xpath('dd[2]/div/div[contains(@class,"l redc00")]')[0] if node is not None: merit=node.xpath('following-sibling::*[1]/text()')[0].extract() else: merit='' except: merit=''try: #先获取是否有‘缺点’元素，若有则定位‘缺点’元素的下一个兄弟节点，即‘缺点评语’，若无则为空 node=info.xpath('dd[2]/div/div[contains(@class,"l hei666")]')[0] if node is not None: demerit=node.xpath('following-sibling::*[1]/text()')[0].extract() else: demerit='' except: demerit=''try: #先获取是否有‘综述’元素，若有则定位‘综述’元素的下一个兄弟节点，即‘综述评语’，若无则为空 node=info.xpath('dd[2]/div/div[contains(@class,"l")]')[0] if node is not None: summary=node.xpath('following-sibling::*[1]/text()')[0].extract() else: summary='' except: summary=''flower=info.xpath('dd[2]/div[contains(@class,"apply")]/a[3]/span/text()')[0].extract() #获取鲜花数 brick=info.xpath('dd[2]/div[contains(@class,"apply")]/a[4]/span/text()')[0].extract() #获取板砖数#创建Item item=CarspiderItem() item['file']=response.meta['file'] item['u_id']=uid item['u_score']=score item['u_merit']=merit item['u_demerit']=demerit item['u_summary']=summary item['u_flower']=flower item['u_brick']=brick#生成Item yield item#获取`下一页`元素，若有则回调`parse_remark`第三层解析方法，即继续获取下一页用户评论数据 #定位`下一页`元素 next_pages=response.css('body > div.newbox > div > div.xgo_cars_w760.l > div.xgo_dianping_infos.mb10 > div.xgo_cars_dianping > div > div > a.next') for next_page in next_pages: #若有`下一页`元素，则拼接`下一页`元素链接，并回调第三层解析方法，用来获取下一页用户评论数据 if next_page is not None: next_page_link=next_page.xpath('@href')[0].extract() next_page_link='http://www.58che.com'+next_page_link file=response.meta['file'] yield scrapy.Request(url=next_page_link, callback=self.parse_remark, meta={'file': file})#将总评分写入文件 def writeScore(self,file,score): with open('/home/t/dataset/carRemark/'+file+'.json','a+') as f: f.write(score+'\n')#将用户评分星级转为5分制分数，类似switch功能 def getScore(self,text): text=text.split(':')[1] #分割文本，原文本格式形如`width:100%`，分割并截取`:`后的文本 return { '100%':5, '80%':4, '60%':3, '40%':2, '20%':1, '0%':0 }.get(text)

【解析】

#定位到用户评论元素 infos=response.css('body > div.newbox > div > div.xgo_cars_w760.l > div.xgo_dianping_infos.mb10 > div.xgo_cars_dianping > div > dl')

此句代码定位的元素如下图所示，定位到的是评论页面每条评论的元素整体。

for info in infos: uid=info.xpath('dd[1]/strong/a/text()')[0].extract() #获取用户ID score=info.xpath('dd[1]/div/div/@style')[0].extract() #获取用户评分星级 score=self.getScore(score) #将用户评分星级转化为5分制评分

uid定位到的元素如下图所示，

score定位到的元素如下图所示，获取score元素的style属性，值形如width:80%，需要通过getScore()方法转换为五分制分数。

try: #先获取是否有‘优点’元素，若有则定位‘优点’元素的下一个兄弟节点，即‘优点评语’，若无则为空 node=info.xpath('dd[2]/div/div[contains(@class,"l redc00")]')[0] if node is not None: merit=node.xpath('following-sibling::*[1]/text()')[0].extract() else: merit='' except: merit=''

先定位是否有优点元素，如下图红框所示，若有该元素，则获取优点元素的下一个兄弟节点内容，如下图蓝框所示，若无则为空。

#获取`下一页`元素，若有则回调`parse_remark`第三层解析方法，即继续获取下一页用户评论数据 #定位`下一页`元素 next_pages=response.css('body > div.newbox > div > div.xgo_cars_w760.l > div.xgo_dianping_infos.mb10 > div.xgo_cars_dianping > div > div > a.next') for next_page in next_pages: #若有`下一页`元素，则拼接`下一页`元素链接，并回调第三层解析方法，用来获取下一页用户评论数据 if next_page is not None: next_page_link=next_page.xpath('@href')[0].extract() next_page_link='http://www.58che.com'+next_page_link file=response.meta['file'] yield scrapy.Request(url=next_page_link, callback=self.parse_remark, meta={'file': file})

解析完上述内容，判断用户评论页面是否有分页，定位是否有下一页元素，如下图红框所示，若有则获取该元素链接，如下图橙框所示。
获取之后，回调parse_remark方法解析下一页的评论页面。

编辑pipelines.py文件

# -*- coding: utf-8 -*-# Define your item pipelines here # # Don't forget to add your pipeline to the ITEM_PIPELINES setting # See: http://doc.scrapy.org/en/latest/topics/item-pipeline.htmlimport json import codecsbaseDir = '/home/t/dataset/carRemark/' class CarspiderPipeline(object): def process_item(self, item, spider): print(item['file']) with codecs.open(baseDir+item['file']+'.json','a+',encoding='utf-8') as f: line=json.dumps(dict(item),ensure_ascii=False)+'\n' f.write(line)return item

编辑settings.py文件

# -*- coding: utf-8 -*-# Scrapy settings for carSpider project # # For simplicity, this file contains only settings considered important or # commonly used. You can find more settings consulting the documentation: # #http://doc.scrapy.org/en/latest/topics/settings.html #http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html #http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.htmlBOT_NAME = 'carSpider'SPIDER_MODULES = ['carSpider.spiders'] NEWSPIDER_MODULE = 'carSpider.spiders'# Crawl responsibly by identifying yourself (and your website) on the user-agent #USER_AGENT = 'carSpider (+http://www.yourdomain.com)'# Obey robots.txt rules ROBOTSTXT_OBEY = False# Configure maximum concurrent requests performed by Scrapy (default: 16) #CONCURRENT_REQUESTS = 32# Configure a delay for requests for the same website (default: 0) # See http://scrapy.readthedocs.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs #DOWNLOAD_DELAY = 3 # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16# Disable cookies (enabled by default) #COOKIES_ENABLED = False# Disable Telnet Console (enabled by default) #TELNETCONSOLE_ENABLED = False# Override the default request headers: #DEFAULT_REQUEST_HEADERS = { #'Accept': 'text/html,application/xhtml+xml,application/xml; q=0.9,*/*; q=0.8', #'Accept-Language': 'en', #}# Enable or disable spider middlewares # See http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html #SPIDER_MIDDLEWARES = { #'carSpider.middlewares.CarspiderSpiderMiddleware': 543, #}# Enable or disable downloader middlewares # See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html #DOWNLOADER_MIDDLEWARES = { #'carSpider.middlewares.MyCustomDownloaderMiddleware': 543, #}# Enable or disable extensions # See http://scrapy.readthedocs.org/en/latest/topics/extensions.html #EXTENSIONS = { #'scrapy.extensions.telnet.TelnetConsole': None, #}# Configure item pipelines # See http://scrapy.readthedocs.org/en/latest/topics/item-pipeline.html ITEM_PIPELINES = { 'carSpider.pipelines.CarspiderPipeline': 300, }# Enable and configure the AutoThrottle extension (disabled by default) # See http://doc.scrapy.org/en/latest/topics/autothrottle.html #AUTOTHROTTLE_ENABLED = True # The initial download delay #AUTOTHROTTLE_START_DELAY = 5 # The maximum download delay to be set in case of high latencies #AUTOTHROTTLE_MAX_DELAY = 60 # The average number of requests Scrapy should be sending in parallel to # each remote server #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 # Enable showing throttling stats for every response received: #AUTOTHROTTLE_DEBUG = False# Enable and configure HTTP caching (disabled by default) # See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings HTTPCACHE_ENABLED = False #HTTPCACHE_EXPIRATION_SECS = 0 #HTTPCACHE_DIR = 'httpcache' #HTTPCACHE_IGNORE_HTTP_CODES = [] #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

【解析】