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    <abstract>In 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.</abstract>
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%0 Conference Proceedings
%T Swahili News Classification: Performance, Challenges, and Explainability Across ML, DL, and Transformers
%A Pandya, Manas
%A Sharma, Avinash Kumar
%A Shukla, Arpit
%Y Lignos, Constantine
%Y Abdulmumin, Idris
%Y Adelani, David
%S Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-257-2
%F pandya-etal-2025-swahili
%X In 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.
%R 10.18653/v1/2025.africanlp-1.30
%U https://aclanthology.org/2025.africanlp-1.30/
%U https://doi.org/10.18653/v1/2025.africanlp-1.30
%P 203-209

Markdown (Informal)

[Swahili News Classification: Performance, Challenges, and Explainability Across ML, DL, and Transformers](https://aclanthology.org/2025.africanlp-1.30/) (Pandya et al., AfricaNLP 2025)

• Swahili News Classification: Performance, Challenges, and Explainability Across ML, DL, and Transformers (Pandya et al., AfricaNLP 2025)

ACL

• Manas Pandya, Avinash Kumar Sharma, and Arpit Shukla. 2025. Swahili News Classification: Performance, Challenges, and Explainability Across ML, DL, and Transformers. In Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025), pages 203–209, Vienna, Austria. Association for Computational Linguistics.