Automated News Article Classification

The exponential growth of digital news content has rendered traditional manual categorization methods inadequate for managing and organizing vast information streams. This paper presents the design and implementation of an automated news classification system that leverages machine learning to efficiently categorize news articles into predefined topics such as politics, sports, business, and lifestyle. The system employs natural language processing (NLP) techniques, including text preprocessing and feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), to convert unstructured text into a structured numerical format suitable for machine learning. Three algorithms—Logistic Regression, Random Forest Classifier, and Multinomial Naive Bayes—are trained on labeled datasets to accurately predict categories for new, unseen articles, thereby automating the classification process and enhancing scalability.