HausaNLP at SemEval-2025 Task 11: Hausa Text Emotion Detection
This work addresses emotion detection for Hausa speakers, but it is incremental as it applies an existing method to a new dataset.
The paper tackled multi-label emotion detection in Hausa, a low-resource language, by fine-tuning AfriBERTa, achieving a validation accuracy of 74.00% and an F1-score of 73.50%.
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.