Bidirectional GRU for Aspect-Based Sentiment Classification in Multi-Dimensional Review Analysis - Redjeki, Sri; Joshi, Basanto; Situmorang, Alfonso; Guntara, Muhammad; Candra Nursari, Sri Rezeki; Kusumawati, Dara (ZERO: Jurnal Sains, Matematika dan Terapan, 2025)
Traditional markets in Yogyakarta face mounting pressure from modernization and digital retail competition, yet user-generated reviews remain underutilized. This study applies Aspect-Based Sentiment Analysis (ABSA) with a Bidirectional Gated Recurrent Unit (BiGRU) on 9,222 annotated reviews from nine markets (2016-2024). BiGRU was chosen not only for its efficiency but also for its robustness in low-resource, multilingual settings with informal expressions, where transformer models often require larger datasets and compute. The best configuration with 64 GRU units and a 70:15:15 split achieved 83.4% accuracy (95% CI: ±1.2%) and an F1-score of 0.813, surpassing baselines such as Na