This Python project automates the process of scraping, processing, and publishing news content from major websites like Reuters, TechCrunch, Forbes, ZDNet, VentureBeat, Engadget, and TechXplore. The tool scrapes various data points, such as titles, content, images, and keywords, ensuring that all news items are at least 24 hours old and free of duplicates. Processed data is edited using GPT models for improved readability and SEO optimization before being uploaded to a WordPress website.
Final Output: Live News on WordPress
This section showcases the final result of the automated process, where news articles scraped from multiple sources are published on a WordPress website. Each post is enhanced for SEO using GPT models to optimize the content, meta description, and focus keywords. The screenshots display the homepage featuring the latest news updates and the detailed post page, reflecting how the data is presented to users.
Project Information
Code Implementation
In this section, we dive into the key components of the Python code that powers the entire workflow. The code covers various aspects such as web scraping using BeautifulSoup, data filtering to ensure news articles are up-to-date and non-duplicative, content enhancement through GPT models, and seamless integration with GitHub for continuous data updates. The implementation ensures an efficient and automated end-to-end process for gathering, editing, and publishing news content.
Extraction Results
This section provides a look at the extracted data in CSV format before it is published. The CSV file contains essential fields such as link, title, content, publish date, meta description, and focus keywords, all cleaned and optimized for further processingv. The screenshot illustrates how the data is structured, ready to be uploaded to WordPress through the WP Ultimate CSV Importer Pro plugin for scheduled publishing.
