How is Web Crawling Used in Data Science by@davisdavid

How is Web Crawling Used in Data Science

A web crawler is typically operated by search engines such as Google, Bing, and Yahoo. Web crawlers can receive a search query and apply a search algorithm to search and provide relevant information in response to the search queries by using search engines. Many companies use web crawlers to collect posts and comments on various social media platforms such as Facebook, Twitter and Instagram. Web crawling is used to collect stock prices data from different platforms for different periods (for example 54 weeks, 24 months e.t.c)
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Davis David

Data Scientist | AI Practitioner | Software Developer. Giving talks, teaching, writing.

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Web crawling is the technique used to collect a huge amount of data from different websites and learn what every webpage on the website is all about. The collected data can help you to retrieve specific information that you need.

A web crawler is typically operated by search engines such as Google, Bing, and Yahoo. The goal is to index the content of different websites all over the internet so that they can appear on the search engine result whenever a person tries to find something on the web.

Web crawlers can receive a search query and apply a search algorithm to search and provide relevant information in response to the search queries by using search engines.

Note: A web crawler is sometimes known as a spider.

Why is Web Crawling Important to Data Science

Everything we do nowadays generate data, therefore we are data agents. There are 4.66 billion active internet users around the world that have created 2.5 quintillion data bytes daily.

The internet has a lot of data that can be used in the Data Science ecosystem to create different solutions that can solve business problems.

Web crawling plays a big role in the data science ecosystem to discover and collect data that can be used in a data science project. Many companies rely on a web crawler to collect data about their customers, products, and services on the web.

Data science project starts by formulating the business problem to solve and then followed by the second stage of collecting the right data to solve that problem. In this stage, you can use web crawlers to collect the data on the internet that you need for your data science project.

Use Cases of Web Crawling in Data Science Projects

Web crawling is an integral part of your data science project. The following are some of the use cases of using web crawling in different data science projects.

1. Collect Social Media Data for Sentiment Analysis

Many companies use web crawling to collect posts and comments on various social media platforms such as Facebook, Twitter and Instagram. Companies use the collected data to assess how their brand is performing and discover how their products or services are reviewed by their customers, it can be a positive review, negative review or neutral.

2. Collect Financial Data for Stock Prices Forecasting

The stock market is full of uncertainty, therefore stock price forecasting is very important in business. Web crawling is used to collect stock prices data from different platforms for different periods (for example 54 weeks, 24 months e.t.c).

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The stock prices data collected can be analyzed to discover trends and other behaviors. You can also use the data to create predictive models to predict future stock prices. This will help stockbrokers to make decisions for their business.

3. Collect Real Estate data for Price Estimation

Evaluating and calculating the price of real estate is time-consuming. Many real-estate companies use data science to create a predictive model to predict the prices of properties by using historical data.

These historical data are collected from multiple sources on the web and extracted useful information by using web crawlers. Companies also use these data to support their marketing strategy and make the right decisions.

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For example, an American online real estate company called Zillow has used data science to determine prices based on a range of publicly available data on the web.

How To Build a Web Crawler by Yourself

In this part, you will learn the steps required to find and collect data from Twitter using the Twint Python library. Twint is an open-source python package that allows you to collect tweets from Twitter without Twitter’s API.

You can collect data from specific users, tweets relating to certain topics, hashtags, and trends. you can fetch almost all Tweets (Twitter API limits to last 3200 Tweets only).

Install Twint
The easiest way to install Twint from PyPI is by using pip:

pip install twint

Note: the command above will automatically install all dependencies of Twint.

Import Packages
The first step is to import Twint into your Python file or notebook. You will also need to import and initialize net_asyncio to handle runtime errors from the notebook.

import twint
import nest_asyncio
net_asyncio.apply()

Configure Twint Object
You need to configure the Twint object to find and identify the types of data it will collect from twitter. In this example, Twint will search and extract tweets related to a Google product called “Google Home”. The data collected can be used for sentiment analysis.

You can also configure the following:-

  • Language of a tweet
  • Number of tweets to collect
  • Specify how you will store the data after being downloaded. For example, save tweets on a CSV file.
  • Configure the name of the CSV file.
​​# Configure

c = twint.Config()

c.Search = "google home"
c.Lang = "en"
c.Limit = 100
c.Store_csv = True
c.Output = "/Users/davisdavid/Downloads/google_home_tweets.csv"

Extract Tweets Data
Finally, you can run the Twint object to extract data from Twitter by using the twint.run.Search method. It will extract tweets according to the configuration you have specified.

# Run

twint.run.Search(c)

In this example, it will extract 100 tweets by using the search query called “Google Home” and save the data in a CSV file called “google_home_tweets.csv”.

The CSV file will contain different fields of extracted data search as date, time, timezone, user_id, username, tweet itself, language, hashtags, link of the tweet, geo, and others.

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Recommended No-Code Web Crawling Tools

Sometimes it is not feasible to manually collect data from the web for various reasons such as no time, too slow, or complex configuration. Fortunately, you can use different no-code web crawling tools to automate the collection of data from the web for your data science project.

There are so many no-code web crawling tools available to choose but in the section, you will learn the top 3 free no-code tools you can use in your data science project.

1. Octoparse
Octoparse is a visual software tool that you can use to extract different types of data from the web without writing codes. It also has various features that make it easier to collect data within a short period.

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If you are a beginner, Octoparse is the right no-code tool for you because it offers step-by-step instructions that you can follow to configure your own task and collect the data that you want.

The free version of Octoparse offers:

  • 2 concurrent tasks on a local machine.
  • 10 crawlers to extract the data you want for your projects.
  • Crawl an unlimited number of pages for your tasks.
  • Extract different types of data such as links, text, and data from list/table pages.
  • Store extracted data to the cloud platform.
  • Download extracted data in CSV, HTML or TXT file.

Octoparse is available for both Windows and macOS users. You can click here to download Octoparse and start collecting data for your project.

2. Parsehub
Parsehub 
is another easy-to-learn visual web crawling tool that is simple, friendly to use, powerful and flexible to extract data from the web. It offers an easy-to-use interface to set your run and automatically extract millions of data points from any website in minutes.

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You can access the extracted data by using API, CSV/Excel files, Google Sheets, and Tableau.

The free version of Parsehub offers the following features:

  • Crawl 200 pages per run/task.
  • You can create 5 public projects.
  • You get limited support.
  • Data retention for 14 days.

Parsehub is available for Windows,macOS, and Linux users. You can click here to download Parsehub and start collecting data for your project.

3. Webscraper
Webscraper 
is a web crawling tool that does not require you to write code and it runs within the browser as an extension. You can use this tool to collect data from the web on an hourly, daily, or weekly basis. It can also automatically export data to Dropbox, Google sheets, or Amazon S3.

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Webscraper.io offers the following features.

  • Point and click interface to Configure scraper and extract the data you want.
  • Extract data from dynamic websites with multiple levels of navigation.
  • It can also navigate a website on all levels.
  • Built for the modern web to handle full JavaScript execution, waiting for Ajax requests, pagination, and Page scroll down.
  • Modular selector system to tailor data extraction to different site structures.
  • Download data in CSV, JSON, and XLSX formats.

The Webscraper extension is available in both the Chrome store and Firefox browser Add-ons. After installation, you should restart the browser to make sure the extension has fully loaded.

Conclusion

Data has become the basis to support decision-making in both profit and non-profit organizations. Therefore, web crawling has found its applications in the data science ecosystem and having that in mind, it is definitely recommended to have skills in web crawling if you plan to become a Data Scientist.

In this article, you learned what is web crawling and why it plays an integral part in data science. You have also learned recommended no-code web crawling tools to extract data from the web in minutes.

If you learned something new or enjoyed reading this article, please share it so that others can see it. Until then, see you in the next post!

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