An explainable AfroXLMR approach for multi-label emotion classification of Amharic social media text with dataset release.

Authors

Yeshimebet Bayu, Demeke Endalie, Tesfa Tegegne

Published in

Scientific reports. Jul 08, 2026. Epub Jul 08, 2026.

Abstract

Emotion detection from social media is crucial for understanding human emotions across languages. However, for low-resourced languages such as Amharic, the lack of annotated data makes this task challenging. Additionally, most current models use black-box methods that obscure whether predictions rely on linguistically meaningful cues. To address these gaps, this study proposes a multi-label emotion classification model for Amharic by fine-tuning AfroXLMR. To enhance transparency, we integrate explainable artificial intelligence (XAI) into the framework. We compiled and annotated a new dataset of 22,000 unique social media comments across eight emotion categories for training, validation, and testing. The data was split into 80% for training, 10% for validation, and 10% for testing. The proposed model achieved a recall of 87% and a Hamming loss of 0.08. To interpret its predictions, we applied Local Interpretable Model-agnostic Explanations (LIME). We also evaluated the model against several state-of-the-art baselines, including XLM-R base, mBART, BiLSTM, LSTM, CNN, and AfriBERTa. The results show that our approach outperformed each baseline, achieving F1-score improvements of 5% over XLM-R base, 3% over mBART, 5% over BiLSTM, 7% over LSTM, 9% over CNN, and 2% over AfriBERTa. Bootstrapped statistical significance testing confirms that these improvements are robust and not attributable to random variation. In conclusion, the fine-tuned AfroXLMR model demonstrates promising performance in Amharic multi-label emotion classification. Building on this success, next steps could involve exploring more advanced fine-tuning strategies and expanding our datasets to strengthen both performance and the model's ability to generalize across diverse Amharic contexts.

PMID:
42420540
Bibliographic data and abstract were imported from PubMed on 09 Jul 2026.

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