Bidirectional GRU for Aspect-Based Sentiment Classification in Multi-Dimensional Review Analysis

Authors

  • Sri Redjeki Universitas Teknologi Digital Indonesia
  • Basanto Joshi Tribhuvan University
  • Alfonso Situmorang Universitas Methodist Indonesia
  • Muhammad Guntara Universitas Teknologi Digital Indonesia
  • Sri Rezeki Candra Nursari Universitas Pancasila
  • Dara Kusumawati Universitas Teknologi Digital Indonesia

DOI:

https://doi.org/10.30829/zero.v9i2.25754

Keywords:

Aspect-Based Sentiment Analysis, Bidirectional GRU, Deep Learning, Market Review, Text Mining.

Abstract

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

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Published

2025-10-06

Issue

Vol. 9 No. 2 (2025): Zero: Jurnal Sains Matematika dan Terapan

Section

Articles

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