Swahili News Classification: Performance, Challenges, and Explainability Across ML, DL, and Transformers

AbstractIn this paper, we propose a comprehensive framework for the classification of Swahili news articles using a combination of classical machine learning techniques, deep neural networks, and transformer-based models. By balancing two diverse datasets sourced from Harvard Dataverse and Kaggle, our approach addresses the inherent challenges of imbalanced data in low-resource languages. Our experiments demonstrate the effectiveness of the proposed methodology and set the stage for further advances in Swahili natural language processing.