SPAN-LEVEL ASPECT-BASED SENTIMENT TRIPLET ANALYSIS IN GOVERNMENT APPLICATION REVIEWS - Feza Raffa Arnanda; Lya Hulliyyatus Suadaa; Avi Rudianita Indah Dg Widya; Setia Irham Pramana (UPT Publication and Journal Management, Islamic University of Kalimantan, 2026)
The government is enhancing digital public services through mobile applications in line with the Electronic-Based Government System (SPBE) 2018–2025 vision. To support continuous innovation, the Ministry of Administrative and Bureaucratic Reform (Kemenpan-RB) organize the Public Service Innovation Competition (KIPP). Understanding user complaints is essential, and aspect-based sentiment analysis, particularly Span-Level Aspect Sentiment Triplet Extraction (Span-ASTE), was applied to analyze government app reviews. A domain-specific dataset was developed with a Cohen’s Kappa of 0.817, indicating strong annotation reliability. IndoBERT-large achieved the highest F1-score of 0.76, while IndoBERT-lite-base provided an efficient alternative with an F1-score of 0.727. An aspect categorization model reached 0.86 accuracy. These models aim to improve public services, strengthen SPBE implementation, and enhance Indonesia’s E-Government Development Index ranking.