HausaNLP at SemEval-2025 Task 11: Hausa Text Emotion Detection

Sani Abdullahi Sani, Salim Abubakar, Falalu Ibrahim Lawan, Abdulhamid Abubakar, Maryam Bala
6/19/2025
cs.CL

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Abstract

This paper presents our approach to multi-label emotion detection in Hausa, a low-resource African language, for SemEval Track A. We fine-tuned AfriBERTa, a transformer-based model pre-trained on African languages, to classify Hausa text into six emotions: anger, disgust, fear, joy, sadness, and surprise. Our methodology involved data preprocessing, tokenization, and model fine-tuning using the Hugging Face Trainer API. The system achieved a validation accuracy of 74.00%, with an F1-score of 73.50%, demonstrating the effectiveness of transformer-based models for emotion detection in low-resource languages.

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Cite this paper

@article{sani2025hausanlp,
  title  = {HausaNLP at SemEval-2025 Task 11: Hausa Text Emotion Detection},
  author = {Sani Abdullahi Sani and Salim Abubakar and Falalu Ibrahim Lawan and Abdulhamid Abubakar and Maryam Bala},
  year   = {2025},
  eprint = {2506.16388},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2506.16388v2}
}

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