简单NLP分析套路（1）----语料库积累之3种简单爬虫应对大部分网站

流川疯

发布于 2019-01-17 17:26:38

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发布于 2019-01-17 17:26:38

文章被收录于专栏：流川疯编写程序的艺术

近期听课的思考

近期有机会听了听天善智能的课程《自然语言处理之AI深度学习顶级实战课程》慢慢的有一些心得，以后有机会慢慢给大家分享出来。

为什么微软称NLP 为人工智能“皇冠上的明珠”？----认知智能

深度学习在自然语言处理的通用步骤

论文的阅读，最新算法的研究
算法的大概方向的评估训练和确定
训练数据的收集，清洗以及数据的预处理
算法实现，系统设计，参数调优，模型升级
模型效果评估与部署

语料库的记录

其实对于很多公司来说，要做NLP的一个最大的问题就是语料库的积累，包括词向量，知识库等等。这些东西最好的来源是什么呢？–爬虫。爬虫最常用的三种手段： 1.urllib.request 构造页面post 请求 2.scrapy 如果有非常详细的网站树形结构，使用该框架爬取非常快捷方便 3.selenium 自动化测试利器，针对动态请求，url没有变化的网站类型有奇特疗效以下分别针对上述三种爬取方式给出实例代码

3种爬虫

urllib.request + BeautifulSoup

主要思路，遍历分页列表–>获取每一页的博客链接–>依次爬取博客内容

# encoding: utf-8
'''
@author: season
@contact: 

@file: spider_for_csdn.py
@time: 2018/10/16 21:32
@desc:
'''
import io
import os
import sys
import urllib
from urllib.request import urlopen
from urllib import request
from bs4 import BeautifulSoup
import datetime
import random
import re
import requests
import socket

socket.setdefaulttimeout(5000)  # 设置全局超时函数

sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='gb18030')
headers1 = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:23.0) Gecko/20100101 Firefox/23.0'}
headers2 = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/45.0.2454.101 Safari/537.36'}
headers3 = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11'}

# 得到CSDN博客某一个分页的所有文章的链接
articles = set()


def getArticleLinks(pageUrl):
    # 设置代理IP
    # 代理IP可以上http://ip.zdaye.com/获取，此处不应该硬编码
    proxy_handler = urllib.request.ProxyHandler({'post': '49.51.195.24:1080'})
    proxy_auth_handler = urllib.request.ProxyBasicAuthHandler()
    opener = urllib.request.build_opener(urllib.request.HTTPHandler, proxy_handler)
    urllib.request.install_opener(opener)
    # 获取网页信息
    req = request.Request(pageUrl, headers=headers1 or headers2 or headers3)
    html = urlopen(req)
    bsObj = BeautifulSoup(html.read(), "html.parser")
    global articles
 # 正则表达式匹配每一篇文章链接(比较硬编码h4 这个四级标题里面藏了所有链接)
    for articlelist in bsObj.findAll("h4"): 
        # print(articlelist)
        if 'href' in articlelist.a.attrs:
            if articlelist.a.attrs["href"] not in articles:
                # 遇到了新界面
                newArticle = articlelist.a.attrs["href"]
                # print(newArticle)
                articles.add(newArticle)
                #print(newArticle)


# 得到CSDN博客某个博客主页上所有分页的链接，根据分页链接得到每一篇文章的链接并爬取博客每篇文章的文字
pages = set()


def getPageLinks(bokezhuye):
    # 设置代理IP
    # 代理IP可以上http://ip.zdaye.com/获取
    proxy_handler = urllib.request.ProxyHandler({'post': '49.51.195.24:1080'})
    proxy_auth_handler = urllib.request.ProxyBasicAuthHandler()
    opener = urllib.request.build_opener(urllib.request.HTTPHandler, proxy_handler)
    urllib.request.install_opener(opener)
    # 获取网页信息
    req = request.Request(bokezhuye, headers=headers1 or headers2 or headers3)
    html = urlopen(req)
    bsObj = BeautifulSoup(html.read(), "html.parser")
    # 获取当前页面(第一页)的所有文章的链接
    getArticleLinks(bokezhuye)
    # 去除重复的链接
    global pages
    for pagelist in bsObj.findAll("a", href=re.compile("^/([A-Za-z0-9]+)(/article)(/list)(/[0-9]+)*$")):  # 正则表达式匹配分页的链接
        if 'href' in pagelist.attrs:
            if pagelist.attrs["href"] not in pages:
                # 遇到了新的界面
                newPage = pagelist.attrs["href"]
                # print(newPage)
                pages.add(newPage)
                # 获取接下来的每一个页面上的每一篇文章的链接
                newPageLink = "http://blog.csdn.net/" + newPage
                getArticleLinks(newPageLink)
                # 爬取每一篇文章的文字内容
                for articlelist in articles:
                    newarticlelist = "http://blog.csdn.net/" + articlelist
                    print(newarticlelist)
                    getArticleText(newarticlelist)



####获取到每一个分页列表的所有文章
str_page_url_prefix = 'https://blog.csdn.net/wangyaninglm/'

list_page_str = str_page_url_prefix + 'article/list/'

#输入分页数据量,我的博客17页
for i in range(1,18):
    getPageLinks(list_page_str+ str(i))

page_url_list = []
page_url_pattern = "(" + str_page_url_prefix + "article/details)(/[0-9]+)*$"

# 把不符合的格式链接去除
for page_link in articles:

    if re.match(page_url_pattern,page_link):
        page_url_list.append(page_link)
    else:
        pass


print(len(page_url_list))

dict_page_content = {'title':'','content':''}
list_page_content = []

import spider_for_403

for url in page_url_list:
    spider_for_403.get_Content(url,'blog-content-box','title-article','article_content')

在爬取的过程中发现403报错，于是写了下面文件，更多的浏览器头

import urllib
import urllib.request
import random
from bs4 import BeautifulSoup

import urllib.error

my_headers = [
    "Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36",
    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/35.0.1916.153 Safari/537.36",
    "Mozilla/5.0 (Windows NT 6.1; WOW64; rv:30.0) Gecko/20100101 Firefox/30.0",
    "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_2) AppleWebKit/537.75.14 (KHTML, like Gecko) Version/7.0.3 Safari/537.75.14",
    "Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.2; Win64; x64; Trident/6.0)"
]

import re

#windows 创建文件替换特殊字符
def validateTitle(title):
    rstr = r"[\/\\\:\*\?\"\<\>\|]"  # '/ \ : * ? " < > |'
    new_title = re.sub(rstr, "_", title)  # 替换为下划线
    return new_title.replace('\r','').replace('\n','').replace('\t','')

#csdn 的网页解析
def get_Content(url,contend_box_id,title_id,contend_id):
    try:
        randdom_header = random.choice(my_headers)

        req = urllib.request.Request(url)

        req.add_header("User-Agent", randdom_header)
        req.add_header("GET", url)

        response = urllib.request.urlopen(req)

        bsObj = BeautifulSoup(response.read(), "html.parser")
    # 获取文章的文字内容
    # 获取网页信息
    #此处逻辑应为：首先获取文章box 的id 之后获取，title 的，之后是content 的
    # 将每一篇博客分别保存为一个文件
        title = bsObj.findAll(name='h1',attrs={'class':title_id})
        str_title = validateTitle(title[0].get_text() + '.txt')
        print(str_title.encode('gbk'))
        f_blog = open('blog//' + str_title, 'w', encoding='utf-8')
# 正则表达式匹配博客包含框 标签
#内容,注意此处用了bsobj 因为如果缩小范围可能找不到（第二个循环）
        for content_box in bsObj.findAll(name='div',attrs={'class':contend_box_id}):  

            for contend in bsObj.findAll(name='div',id = contend_id):

                str_content = 'content' + '\n'+ contend.get_text() + '\n'
                f_blog.write(str_content)

        f_blog.close()

        response.close()  # 注意关闭response
    except OSError as e:
        print(e)
    except urllib.error.URLError as e:
        print(e.reason)

# url = 'https://blog.csdn.net/wangyaninglm/article/details/45676169'
# #
# get_Content(url,'blog-content-box','title-article','article_content')

效果：

scrapy 与xpath

在pycharm 中调试 scrapy

from scrapy import cmdline
cmdline.execute('scrapy crawl Hospital'.split())

写好spider 的解析函数

#

class HospitalSpider(Spider):
    i = 2;
    name = 'Hospital'
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36',
    }
    jianjie = 'jianjie.html'
    base_url = 'https://yyk.99.com.cn'
    def start_requests(self):
        url = 'https://yyk.99.com.cn/city.html'
        yield Request(url, headers=self.headers)

    def parse(self, response):
        # .re(r'[\u4e00-\u9fa5]{2,4}')匹配中文字符和长度，31为表格前31个后面的值包括了英文字母排序的值
        hospitals_sub_url = response.xpath(
            '//div[@class="m-clump"]//dt/a[@href]/@href').extract()[:31]

        for url in hospitals_sub_url:
                url = str(self.base_url + url)
                yield Request(url, callback=self.parse_dir_urls)


    def parse_dir_urls(self, response):
        hospitals_sub_url = response.xpath(
            '//div[@class="m-table-2"]//td/a[@href]/@href').extract()

        for url in hospitals_sub_url:
            url = str(self.base_url + url+ self.jianjie)
            yield Request(url, callback=self.parse_dir_contents)

    def parse_dir_contents(self, response):
        item = HospitalspiderItem()

        item.item_dict['更新日期'] = response.xpath('//div[@class="crumb"]//font/text()').extract()

        #xpath：/html/body/div[6]/div[3]/div[1]/div[1]/table/tbody/tr[1]/td[4]/a
        #此表格含有tbody 标签，不是很好处理，使用跳转语法.单双斜杠都可
        item.item_dict['所在地区'] = response.xpath('//table[@class="present-table"]//tr[1]/td[4]/a/text()').extract()
        item.item_dict['简介'] = response.xpath('//div[@class="present-wrap1"]//div[@id="txtintro"]').extract()


        yield item

pipeline 对于依次爬取的item 进行处理，此处写成csv ，参照item 类进行数据持久化 pipeline

# -*- 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 re
from HospitalSpider import items

class HospitalspiderPipeline(object):
    csv_head = items.HospitalspiderItem()
#正则表达式去除html 标签（在scrapy 爬取过程中有些标签lxml 没法解析，带着标签爬下来了）
    def clean_html(self,str):
        reg = re.compile('<[^>]*>')
        return reg.sub('', str)
#csv 增加引号
    def add_yinhao(self, str):
        if str:
            return '"' + str + '"'
        else:
            return ''
#对每一项转换成str，去除html 标签,这块的参数应该怎么写
    def write_csv_line(self, item):
        str_row = ''
        for i in item.item_list:
            if item.item_dict[i]:
                str_row = str_row + self.add_yinhao(self.clean_html(str(item.item_dict[i][0]))) + ','

        str_row = str_row.strip(',').replace('\r','').replace('\n','').replace('\t','').replace(' ','') + '\n'

        return str_row

    def __init__(self):
        pass

    def open_spider(self, spider):
        self.file = open('hospital.csv', 'w', encoding='utf-8')
        str_row = ''
        #写文件头
        for i in self.csv_head.item_list:
            str_row = str_row + '"' +i+'"'+','
        self.file.write((str_row.strip(',')+'\n'))



    def process_item(self, item, spider):


        self.file.write(self.write_csv_line(item))



    def close_spider(self, spider):
        # 关闭爬虫时顺便将文件保存退出
        self.file.close()

修改 settings.py 文件

ITEM_PIPELINES = {
   'HospitalSpider.pipelines.HospitalspiderPipeline': 300,
}

使用selenium 模拟浏览器行为

# encoding: utf-8
'''
@author: season
@contact:

@file: main.py
@time: 2018/11/16 14:24
@desc:
'''

import selenium

from selenium import webdriver
import file_operator



#此处使用chrome 复制的xpath 非常准确，因为直接使用了chrome 的webdriver



#获取每一页申请的登记号的详细信息
#str_xpath  = '//tr[contains(@style, " color:#535353")]/td[2]'
def get_Page_all_detail(handle_web_driver,str_xpath):
    list_diag_test = handle_web_driver.find_elements_by_xpath(str_xpath)
    list_Registration_number = []
    #获取所有登记号
    for element in list_diag_test:
        list_Registration_number.append(element.text)

    #已经爬取过的登记号就不爬了
    list_already_have = file_operator.all_pure_file_name_without_extension(r'./html/','.html')

    list_Registration_number = file_operator.sub_list(list_already_have,list_Registration_number)

    #找到所有的登记号的细节
    for Registration_number in list_Registration_number:
        handle_web_driver.find_element_by_link_text(Registration_number).click()
        handle_web_driver.implicitly_wait(1)
        #保存html
        with open(r'./html/'+Registration_number+'.html','w',encoding='utf-8') as html_file:
            page_html = handle_web_driver.page_source
            html_file.write(page_html)


        handle_web_driver.back()


#打开入口链接，设置相关疾病,逐页爬取，翻页
def send_click(url):

    browser = webdriver.Chrome()

    browser.get(url)
    browser.implicitly_wait(1)

    str_xpath = '//tr[contains(@style, " color:#535353")]/td[2]'

#找到共有多少页
    #// *[ @ id = "searchfrm"] / div / div[4] / div[1] / a[3]
    next_page_element_number = int(browser.find_element_by_xpath('// *[ @ id = "searchfrm"] / div / div[4] / div[1]/a[3]').text)

    for index in range(0,next_page_element_number):
        get_Page_all_detail(browser, str_xpath)
        next_button = browser.find_element_by_xpath('//input[contains(@class, "page_next ui-button ui-widget ui-state-default ui-corner-all")]')
        next_button.click()


#函数返回本页没有被爬去的页面（断点续爬）


def main():
    #设置start url 搜索内容
    str_url_base = 'http://www.search.keywords='
    str_diagnosis = '***'
    send_click(str_url_base+str_diagnosis)

if __name__ == '__main__':
    main()

新的改变

我还没写完程序，后序代码和过程逐步贴上来主要计划是，使用我自己的博客作为语料进行，词云，tf-idf ，textrank 等算法的分析

本文参与腾讯云自媒体同步曝光计划，分享自作者个人站点/博客。

原始发表：2018年10月28日，如有侵权请联系 cloudcommunity@tencent.com 删除

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如有侵权，请联系 cloudcommunity@tencent.com 删除。

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