How to Build a Web Crawler– A Guide for BeginnersTuesday, June 04, 2019
As a newbie, I built a web crawler and extracted 20k data successfully from the Amazon Career website. How can you set up a crawler and create a database which eventually turns to your asset at No Cost? Let's dive right in.
What is a web crawler?
A web crawler is an internet bot that indexes the content of a website on the internet. It then extracts target information and data automatically. As a result, it exports the data into a structured format (list/table/database).
Why do you need a Web Crawler, especially for Enterprises?
Imagine Google Search doesn't exist. How long will it take you to get the recipe for chicken nuggets without typing in the keyword? There are 2.5 quintillion bytes of data created each day. That said, without Google Search, it's impossible to find the information.
Google Search is a unique web crawler that indexes the websites and finds the page for us. Besides the search engine, you can build a web crawler to help you achieve:
1. Content aggregation: it works to compile information on niche subjects from various resources into one single platform. As such, it is necessary to crawl popular websites to fuel your platform in time.
2. Sentiment Analysis: it is also called opinion mining. As the name indicates, it is the process to analyze public attitudes towards one product and service. It requires a monotonic set of data to evaluate accurately. A web crawler can extract tweets, reviews, and comments for analysis.
3. Lead generation: Every business needs sales leads. That's how they survive and prosper. Let's say you plan to make a marketing campaign targeting a specific industry. You can scrape email, phone number and public profiles from an exhibitor or attendee list of Trade Fairs, like attendees of the 2018 Legal Recruiting Summit.
How to build a web crawler as a beginner?
A. Scraping with a programming language
writing scripts with computer languages are predominantly used by programmers. It can be as powerful as you create it to be. Here is an example of a snippet of bot code.
From Kashif Aziz
Web scraping using Python involves three main steps:
1. Send an HTTP request to the URL of the webpage. It responds to your request by returning the content of webpages.
2. Parse the webpage. A parser will create a tree structure of the HTML as the webpages are intertwined and nested together. A tree structure will help the bot follow the paths that we created and navigate through to get the information.
3. Using python library to search the parse tree.
Among the computer languages for a web crawler, Python is easy-to-implement comparing to PHP and Java. It still has a steep learning curve prevents many non-tech professionals from using it. Even though it is an economic solution to write your own, it's still not sustainable regards to the extended learning cycle within a limited time frame.
However, there is a catch! What if there is a method that can get you the same results without writing a single line of code?
B. Web scraping tool comes in handy as a great alternative.
There are many options, but I use Octoparse. Let's go back to the Amazon Career webpage as an example:
Goal: build a crawler to extract administrative job opportunities including Job title, Job ID, description, basic qualification, preferred qualification and page URL.
1. Open Octoparse and select "Advanced Mode". Enter the above URL to set up a new task.
2. As one can expect, the job listings include detail-pages that spread over to multiple pages. As such, we need to set up pagination so that the crawler can navigate through. To this, click the "Next Page" button and choose "Look click Single Button" from the Action Tip Panel
3. As we want to click through each listing, we need to create a loop item. To do this, click one job listing. Octoparse will work its magic and identify all other job listings from the page. Choose the "Select All" command from the Action Tip Panel, then choose "Loop Click Each Element" command.
4. Now, we are on the detail page, and we need to tell the crawler to get the data. In this case, click "Job Title" and select "Extract the text of the selected element" command from the Action Tip Panel. As follows, repeat this step and get "Job ID", "Description," "Basic Qualification", "Preferred Qualification" and Page URL.
5. Once you finish setting up the extraction fields, click "Start Extraction" to execute.
However, that's not All!
For SaaS software, it requires new users to take a considerable amount of training before thoroughly enjoy the benefits. To eliminate the difficulties to set up and use. Octoparse adds "Task Templates" covering over 30 websites for starters to grow comfortable with the software. They allow users to capture the data without task configuration.
As you gain confidence, you can use Wizard Mode to build your crawler. It has step-by-step guides to facilitate you to develop your task. For experienced experts, "Advanced Mode" should be able to extract the enterprise volume of data. Octoparse also provides rich training materials for you and your employees to get most of the software.
Writing scripts can be painful as it has high initial and maintenance costs. No single web page is identical, and we need to write a script for every single site. It is not sustainable if you need to crawl many websites. Besides, websites likely changes its layout and structure. As a result, we have to debug and adjust the crawler accordingly. The web scraping tool is more practical for enterprise-level data extraction with fewer efforts and costs.
Consider you may have difficulties to find a web scraping tool, I compile a list of most popular scraping tools. This video can walk you through to get your device that fits your needs! Feel free to take advantage of it.
Author: Ashley Ng
Ashley is a data enthusiast and passionate blogger with hands-on experience in web scraping. She focuses on capturing web data and analyzing in a way that empowers companies and businesses with actionable insights. Read her blog here to discover practical tips and applications on web data extraction
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