<![CDATA[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 Naïve Bayes (74.5%) and SVM (77.2%). At the aspect level, security yielded the highest F1-score (0.944), followed by cleanliness (0.904) and culinary (0.838), while “others” scored lowest (0.676). Practically, the findings reveal positive sentiment toward pricing and product availability but highlight concerns about cleanliness and accessibility, offering actionable guidance for market policy.]]>
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