A Review on News Article Classification Using Different Machine Learning Algorithms - Choudhury, Dimple (International Journal of Computer Sciences and Engineering, 2025)
In today`s digital landscape, the majority of textual data is unstructured, requiring automated classification techniques for better organization and usability. News article classification into distinct categories such as politics, technology, crime, education, and entertainment is a crucial task in Natural Language Processing (NLP), with applications in recommendation systems, fake news detection, and content personalization. This review paper presents a comprehensive overview of various machine learning approaches employed for multi-label news classification across multiple languages and domains. It synthesizes methodologies, comparative results, and key insights from numerous studies while identifying prevailing challenges, emerging trends, and future research directions. The work primarily focuses on contrasting traditional machine learning algorithms with advanced deep learning and transformer-based models to provide a broad perspective on the evolution of techniques in this domain.