How to Build a Web Scraper in Python

How to Build a Web Scraper in Python

Quickly scrape, and summarize Google search engine results. Web scraping is an awesome tool for analysts to sift through and collect large amounts of public data. Using keywords relevant to the topic in question, a good web scraper can gather large amounts of data very quickly and aggregate it into a dataset.

Web Scraping

Web scraping is an awesome tool for analysts to sift through and collect large amounts of public data. Using keywords relevant to the topic in question, a good web scraper can gather large amounts of data very quickly and aggregate it into a dataset. There are several libraries in Python that make this extremely easy to accomplish. In this article, I will illustrate an architecture that I have been using for web scraping and summarizing search engine data. The article will be broken up into the following sections…

  • Link Scraping
  • Content Scraping
  • Content Summarizing
  • Building a Pipeline

All of the code will be provided herein.


First, we need a way to gather URLs relevant to the topic we are scraping data for. Fortunately, the Python library googlesearch makes it easy to gather URLs in response to an initial google search. Let’s build a class that uses this library to search our keywords and append a fixed number of URLs to a list for further analysis…


Content Scraping

This is arguably the most important part of the web scraper as it determines what data on a webpage will be gathered. Using a combination of urllib and beautiful soup (bs4) we are able to retrieve and parse the HTML for each URL in our Link Scraper class. Beautiful soup lets us specify the tags we want to extract data from. In the case below I am establishing a URL request and parsing the HTML response with bs4 and storing all the information found in the paragraph (

) tags…

Content Summarizing

This is where we create a summary of the text extracted from each page’s HTML residing in our Content Scraper. To do this we will be using a combination of libraries, mainly NLTK. The way in which we are generating the summary is relatively elementary and there are many ways to improve this method — but it's a great start. After some formatting and voiding of filler words, words get tokenized and ranked by frequency generating a few sentences that aim to accurately summarize the article…

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