I recently completed a task as a freelancer that involved mining articles from the Wall Street Journal. The idea was to scrape articles related to "Verizon Communications Inc." that were published between July 2021 and March 2023. I thought it would be a great idea to share the Python script that I developed for this task, which can be used as a base for similar tasks. Let's dive into it!

The script is designed to perform the following steps:

Request for articles' ids - This part of the script is designed to get the IDs of all the articles related to the specified query.

Request for articles' details - Once we have all the IDs, the script then moves on to get detailed data on the articles corresponding to those IDs.

Compile the articles' details - All the fetched article details are then compiled into a Python list.

Data cleaning - After getting all the details, the script then cleans the data, keeping only the necessary fields, and then saves the data into a .csv file.

Alright, let's dive into the specific code snippets and understand them better.

First, we import the necessary Python libraries - requests for handling HTTP requests and json and pandas for handling and storing data.

The function gettingArticleDetails(id,type) uses the IDs fetched in the previous step to generate a GET request to the WSJ search URL for specific article details.

So there you have it! This script can be easily adjusted for any query or website with a similar structure.

Happy Scraping!

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