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Scraping News Websites like CNN & NBC using Python

Scraping Intelligence
Scraping News Websites like CNN & NBC using Python

There is a lot of information on news websites. Every day, more information about the world's most pressing issues is posted on these websites. They are an excellent source of information not only for news but also for other topics such as health, fashion, finance, technology, and gadgets. By scraping news websites, one can find new articles on almost any topic.

The main advantage of scraping news websites and overall data is that you can do it with almost any website — as long as the content is online, you can scrape it, from weather forecasts to government spending, even if the site does not have an API for raw data access. 

Do you only want "health" news articles? There is no problem at all! Do you need blog posts in a specific language? Are you from a specific country? You've got it! It is a simple and cost-effective solution for obtaining data from the web that will save you a lot of time and money if done "sustainably" so you can focus on what to do with the obtained data.

Web Scraping News Articles in Python

Scraping news articles can provide valuable data for businesses and organizations, but as previously stated, it can take a long time to do so manually. This is why businesses use Python programs to automatically collect, save, and analyze data from news sites.

Scraping news articles and other websites on the internet necessitates more complex code than a simple "print" command. However, web scraping libraries such as BeautifulSoup, Requests, Selenium, and others have made it easier to write web scraping programs. These libraries contain program code that allows you to connect to publicly accessible websites and automatically scrape and download data.

You can use the Scrapy library to create, run, and deploy web scrapers in the cloud. These scrapers search for website data on your behalf by sending requests to URLs you specify in the program. It then uses a CSS selector to loop through the data elements from the pages you specify.

Scrapy is very fast because it can process asynchronous requests. It's also collaborative and open-source, making it an excellent choice for a web scraping library, particularly for those with little programming experience. Requests, for example, could be just as simple and efficient.

After determining the type of data to scrape, you must run a Python program to scrape and save the data. The steps for scraping the web with Python are as follows:

  • Python should be downloaded and installed.
  • Launch your IDE.
  • Import a library, for example, Scrapy or Requests.
  • To open web pages without a graphical user interface, use a headless browser.
  • Create objects like a page source object and a results object.
  • Use the web scraper class to process the page source object.
  • Take the information from the web pages.
  • Export the data to a CSV or database file.

It is critical to remember that this code is only valid for this specific webpage. When we crawl another site, we should expect different tags and attributes to be utilized to identify items. The method is the same once we've worked out how to locate them.

We can now extract information from a wide range of news sources. The last step is to use the Machine Learning model that we trained in the previous post to forecast the data categories and display a summary to the user. This will be covered in depth in the final post in the series.

Looking for a way to scrape CNN news articles? Please contact us or request a free quote!!

Scraping Intelligence
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