Abstract :

The Rapid Expansion Of Digital Media Has Led To An Overwhelming Volume Of News Articles Being Generated Daily, Making It Challenging To Efficiently Organize, Filter, And Access Relevant Information. Automatic News Topic Classification Has Become Essential For Managing And Structuring This Vast Amount Of Textual Data. This Project Aims To Develop A Machine Learning-based System That Automatically Classifies News Articles Into Predefined Categories Based On Their Content. A Balanced Dataset Consisting Of Multiple News Topics Is Used, And Various Natural Language Processing (NLP) Techniques Are Applied, Including Text Preprocessing, Tokenization, Stop-word Removal, And Feature Extraction. The Processed Text Is Transformed Into Numerical Representations To Enable Effective Model Training. Multiple Classification Algorithms Are Evaluated, Among Which The Random Forest Classifier Demonstrates Strong Performance, Achieving High Accuracy, Precision, And Recall On The Test Dataset. Additionally, Feature Importance Analysis Is Conducted To Identify The Most Influential Words Contributing To Accurate Classification. The Developed Model Provides An Efficient And Scalable Solution For Automatic News Categorization. It Can Be Integrated Into Media Platforms, Search Engines, And Digital Libraries To Improve Content Organization, Enhance User Experience, And Enable Faster Information Retrieval.

Published:

06-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1249-1257

Section:

Articles

License:

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

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