@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T HausaNLP at SemEval-2025 Task 11: Advancing Hausa Text-based Emotion Detection
%A Sani, Sani Abdullahi
%A Abubakar, Salim
%A Lawan, Falalu Ibrahim
%A Abubakar, Abdulhamid
%A Bala, Maryam
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F sani-etal-2025-hausanlp
%X 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.
%U https://aclanthology.org/2025.semeval-1.261/
%P 2014-2019
Markdown (Informal)
[HausaNLP at SemEval-2025 Task 11: Advancing Hausa Text-based Emotion Detection](https://aclanthology.org/2025.semeval-1.261/) (Sani et al., SemEval 2025)
ACL