@inproceedings{sani-etal-2025-hausanlp,
    title = "{H}ausa{NLP} at {S}em{E}val-2025 Task 11: Advancing {H}ausa Text-based Emotion Detection",
    author = "Sani, Sani Abdullahi  and
      Abubakar, Salim  and
      Lawan, Falalu Ibrahim  and
      Abubakar, Abdulhamid  and
      Bala, Maryam",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/transition-to-people-yaml/2025.semeval-1.261/",
    pages = "2014--2019",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our approach to multi-label emotion detection in Hausa, a low-resource African language, as part of 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."
}