H ausa NLP at S em E val-2025 Task 11: Advancing H ausa Text-based Emotion Detection (2025)

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.

## introduction emotion detection in text is a fundamental natural language processing (nlp) task with far-reaching applications in sentiment analysis, social media monitoring, and mental health support. while significant progress has been made in high-resource languages, low-resource languages like hausa remain underrepresented in the nlp landscape. one of the key challenges is the limited availability of annotated hausa datasets for emotion detection, which hinders the development of robust models. this work aims to bridge this gap by leveraging a newly available dataset called brighter by @xcite and fine-tuning a transformer-based model for multi-label emotion detection in hausa. the scarcity of comprehensive emotion lexicons and annotated corpora makes it difficult to develop and evaluate emotion detection systems for lowresource languages @xcite @xcite . additionally, these languages exhibit rich morphological variations, syntax, and semantic differences that are not well-captured by models trained on highresource languages @xcite . the phenomenon of code-mixing, where multiple languages are used within the same text, adds another layer of complexity to emotion detection in linguistically diverse contexts @xcite . our approach leverages afriberta, a transformer model specifically trained on african languages, fine-tuned for multi-class emotion classification. the text data is preprocessed, tokenized using the afriberta tokenizer, and the model is fine-tuned on the brighter hausa dataset. this work contributes to the growing body of research on emotion detection in low-resource languages by demonstrating effective methods for adapting transformer models to hausa. our findings highlight the potential of leveraging pre-trained models like afriberta @xcite for emotion detection tasks in low-resource african languages. ## related work several researchers have proposed innovative approaches to address the challenges of emotion detection in low-resource settings. efforts to create and annotate datasets for low-resource languages include the afrisenti-semeval 2023 shared task, which provided annotated datasets for languages like hausa and igbo @xcite . techniques such as few-shot and zero-shot learning have been employed to augment datasets and improve model performance in data-scarce environments @xcite . pre-trained models like mbert, xlm-r, and banglabert have been fine-tuned for specific low-resource languages, showing improved performance in emotion detection tasks @xcite @xcite . hybrid models combining lexicon features with transformers have also been explored to enhance emotion classification accuracy @xcite . advanced methods such as the use of deep learning models (e.g., cnns, dnns) and hybrid approaches (e.g., tcso-dgnn) have been proposed to better capture the emotional nuances in text (jadon and kumar, 2023; bao and su, 0). afriberta, a transformer model pre-trained on a diverse corpus of african languages, has shown promising results for tasks like text classification and named entity recognition in hausa and other african languages @xcite . our work builds on these advancements by fine-tuning afrib-erta for emotion detection in hausa, addressing the unique challenges of low-resource language processing. future directions in this field include multimodal emotion detection that incorporates additional data modalities (e.g., audio, video) to complement textual analysis (sarbazi-azad et al., 2021; wang and zhang, 2025), developing cross-lingual and multilingual models that can generalize across multiple low-resource languages (raychawdhary et al., 2023b, 2024), and addressing ethical considerations related to data privacy, bias, and the deployment of emotion detection systems in diverse cultural contexts (agarwal and abbas, 2025). ## results and discussion the model performed particularly well on emo-tions like joy and sadness, which were more prevalent in the dataset. however, it struggled slightly with underrepresented emotions such as fear and disgust, likely due to their limited representation in the training data. this highlights the importance of addressing class imbalance in future work, potentially through techniques like data augmentation or weighted loss functions. overall, the results underscore the effectiveness of afriberta for emotion detection in low-resource languages and provide a strong baseline for future research in this domain. ## conclusion in this work, we fine-tuned afriberta for multilabel emotion detection in hausa text, achieving an accuracy of 74.00% and an f1-score of 73.50%. the model performed well on prevalent emotions like joy and sadness but struggled with underrepresented emotions such as fear and disgust, highlighting the challenge of class imbalance. our approach demonstrates the effectiveness of transformer-based models for low-resource language tasks. future work could address class imbalance through data augmentation or weighted loss functions and extend this work to other african languages. this study provides a strong baseline for emotion detection in hausa and contributes to the development of inclusive nlp systems.