🤖 AI Summary
This work addresses the challenge of sentiment classification for low-resource languages like Bengali under domain data scarcity by proposing SentiBanglaBERT, a novel framework employing a two-stage strategy. First, it enhances contextual adaptability through continued pretraining on news-domain corpora; second, it leverages Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning. The approach innovatively integrates domain adaptation with interpretability analysis, utilizing SHAP to uncover the influence of key Bengali morphological features—such as negation suffixes and aspect markers—on sentiment predictions. Experimental results demonstrate that the model achieves performance comparable to strong baselines while maintaining computational efficiency and offering linguistically insightful explanations.
📝 Abstract
Understanding sentiment in low-resource languages remains a key challenge for Natural Language Processing (NLP), particularly when domain-specific data is scarce. In this work, we present SentiBanglaBERT, a two-stage Bengali sentiment classification framework combining domain-adaptive continual pretraining and parameter-efficient fine-tuning. The approach enables contextual adaptation to news-style data while remaining computationally efficient through Low-Rank Adaptation (LoRA). Beyond performance, SentiBanglaBERT integrates SHAP-based interpretability, offering linguistic insights into how Bengali morphological cues, such as negation suffixes and aspectual markers, influence sentiment predictions. Experiments demonstrate stable performance comparable to strong baselines while providing greater transparency and interpretive depth. This framework highlights the potential of domain-adaptive continual learning as a foundation for interpretable, resource-efficient NLP in morphologically rich, underrepresented languages.