Abstract :

In The Digital Era, The Rapid Growth Of Online News Platforms Has Made It Challenging For Users To Access Relevant And Reliable Information Efficiently. Traditional Methods Of Browsing Multiple Websites For News Updates Are Time-consuming And Often Lead To Information Overload. This Project Proposes The Design And Implementation Of A Domestic News Collection System Based On Python, Which Automates The Process Of Gathering, Filtering, And Presenting News Articles From Various Online Sources. The System Utilizes Web Scraping Techniques And Application Programming Interfaces (APIs) To Collect Realtime News Data From Multiple Websites. Natural Language Processing (NLP) Techniques Are Applied To Categorize News Into Different Domains Such As Politics, Sports, Technology, And Entertainment. The Proposed System Is Developed Using Python Libraries Such As BeautifulSoup, Requests, And Pandas For Data Extraction And Processing. Additionally, Machine Learning Algorithms Are Incorporated To Analyze News Content And Recommend Relevant Articles Based On User Preferences. The System Also Includes Features Such As Keyword-based Search, News Summarization, And Duplicate Content Removal To Enhance User Experience. By Integrating These Functionalities, The System Provides A Centralized Platform For Accessing Domestic News In An Organized And Efficient Manner. Experimental Results Demonstrate That The System Effectively Collects And Categorizes News With High Accuracy And Reduced Processing Time. The Proposed Solution Offers A Scalable And User-friendly Approach For News Aggregation, Making It Suitable For Applications In Media Monitoring, Research, And Personalized News Delivery Systems.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1935-1940

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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