Authors: Assistant Professor S.Venkateswara Rao, Kolipaka Divya
Abstract: The rapid expansion of online news platforms has resulted in an enormous volume of digital articles being published every day. Organizing these articles into meaningful categories is essential for improving information retrieval, recommendation systems, and content management. This research presents a machine learning-based framework for automatic news article classification using count vectorization before being transformed into numerical feature vectors. Two supervised outperforming the Naïve Bayes classifier, which records an accuracy of 76%. The proposed framework demonstrates that traditional machine learning algorithms combined with effective text preprocessing techniques provide reliable and efficient performance for automated news categorization.
