Explainable Aspect-Based Sentiment Analysis with Contrast-Aware IndoBERT for Indonesian Public Service Reviews - Muhammad Shihab Fathurrahman Jondien; Taqwa Hariguna; Dhanar Intan Surya Saputra (Forum Kerjasama Pendidikan Tinggi, 2026)
This study presents an Explainable IndoBERT with Contrast-Aware Attention framework for Aspect-Based Sentiment Analysis (ABSA) on Indonesian public service reviews. The proposed model integrates automated aspect labeling using KeyBERT with a contrast-aware mechanism to handle mixed or opposing sentiments within a single sentence. By leveraging IndoBERT as the base transformer, the system captures context-sensitive sentiment cues while maintaining interpretability through attention-based rationale extraction. Experimental results on the SMSA dataset demonstrate an accuracy of 83.4%, with strong precision in positive and negative sentiment detection. The contrast-aware module improves clause-level understanding, while the attention-based explainability module provides transparent, token-level rationales that align with human judgments at an average rate of 87.7%. Although a modest performance decline occurs compared to non-explainable baselines, the proposed model offers significant gains in semantic transparency, making it suitable for evidence-based policy evaluation and citizen feedback monitoring. This research contributes a practical, interpretable, and linguistically grounded solution for explainable sentiment analysis in low-resource languages, advancing the application of responsible AI in public service analytics.