This is the follow up of the Getting Google Search results with Scrapy. In this post, the initial python script for scraping the google search results is completed. The completed script are found in the github.
The program, as described in part 1, obtained the results links from google main page and each links are run separately using Scrapy. In this way, users have more flexibility in obtaining various information from individual websites. At present, only the title and meta contents are scrapped from each website. The other advantage is that is remove further dependency from Google html tag changes.
The disadvantages are that the time taken are relatively longer and descriptions are different from Google’s short summary. I still trying to figure out how to make the contents more meaningful. The present meta content tags are mostly missing for various websites and the contents are not representative of the text.
Dependency of script are Scrapy and yaml (for unicode handling). Both can be downloaded using PIP.
Scripts is divided into 2 parts. The main script for running is from Python_Google_Search.py. The get_google_link_results.py is the scrapy spider for crawling either the google search page or individual websites. The switch depends on the json setting file created.
The spider (get_google_link_results.py) module is a simple script that first get the information from the setting Json file and determine the type of parsing to handle. If the selection is google search links, it will use the following xpath commands to retrieve the all the result links.
sel = Selector(response)
## extract a list of website link related to the search
google_search_links_list = sel.xpath('//h3/a/@href').extract()
google_search_links_list = [re.search('q=(.*)&sa',n).group(1) for n in google_search_links_list\
if re.search('q=(.*)&sa',n)]
If it is parsing all the individual results links, it will use the following xpath contents to scrape the meta information
title = sel.xpath('//title/text()').extract()
if len(title)>0: title = title[0]
contents = sel.xpath('/html/head/meta[@name="description"]/@content').extract()
if len(contents)>0: contents = contents[0]
Example of output obtained by searching “Hello Pandas”. This first 7 results are as below.
####### Google results #####################
Hello Panda – Wikipedia, the free encyclopedia
//en.wikipedia.org/wiki/Hello_Panda
[]
####################
Meiji
//www.meiji.com.au/hellopanda.html
[]
####################
Meiji Hello Panda Chocolate Biscuit, 9.01 Ounce: Amazon.com: Grocery & Gourmet Food
//www.amazon.com/Meiji-Hello-Panda-Chocolate-Biscuit/dp/B000H2DZS0
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####################
Calories in Meiji – Hello Panda Biscuits, with Choco Cream | Nutrition and Health Facts
//caloriecount.about.com/calories-meiji-hello-panda-biscuits-i170737
Curious about how many calories are in Hello Panda Biscuits? Get nutrition information and sign up for a free online diet program at CalorieCount.
####################
Buy Meiji Hello Panda Creamy Chocolate Filled Biscuits at Tofu Cute
//www.tofucute.com/meiji-hello-panda-biscuits-chocolate~p42.html
[]
###################
Japanese Snack Reviews: Meiji “Hello Panda” Cookies (Chocolate)
//japanesesnackreviews.blogspot.sg/2012/10/meiji-hello-panda-cookies-chocolate.html
[]
####################### Results End ##################
The script is still in infant stage. There is a lot of work under construction. The first will be to obtain more meaningful summary from each website. At present, I am thinking of using NLTK but have not really firmed out any solid approach. Any suggestions are greatly appreciated.
Simply Python 