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

Sri Redjeki, Basanto Joshi, Alfonso Situmorang, Muhammad Guntara, Sri Rezeki Candra Nursari, Dara Kusumawati

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 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.

Keywords


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

Full Text:

PDF

References


S. Nosouhian, F. Nosouhian, and A. Kazemi Khoshouei, “A Review of Recurrent Neural Network Architecture for Sequence Learning: Comparison between LSTM and GRU.” pp. 16020–16030, 12-Jul-2021, doi: 10.20944/preprints202107.0252.v1.

M. Zulqarnain, R. Ghazali, Y. Mazwin, M. Hassim, and M. Rehan, “A comparative review on deep learning models for text classification,” vol. 19, no. 1, pp. 325–335, 2020, doi: 10.11591/ijeecs.v19.i1.pp325-335.

M. Zulqarnain, R. Ghazali, M. G. Ghouse, and M. F. Mushtaq, “Efficient Processing of GRU Based on Word Embedding for Text Classification,” vol. 3, pp. 377–383, doi: http://dx.doi.org/10.30630/joiv.3.4.289.

Z. M. Shaikh and S. Ramadass, “Unveiling deep learning powers: LSTM, BiLSTM, GRU, BiGRU, RNN comparison,” Indones. J. Electr. Eng. Comput. Sci., vol. 35, no. 1, p. 263, Jul. 2024, doi: 10.11591/ijeecs.v35.i1.pp263-273.

W. Ali, Y. Yang, X. Qiu, Y. Ke, and Y. Wang, “Aspect-Level Sentiment Analysis Based on Bidirectional-GRU in SIoT,” IEEE Access, vol. 9, pp. 69938–69950, 2021, doi: 10.1109/ACCESS.2021.3078114.

J. Wang, Y. Zhang, L. Yu, and X. Zhang, “Knowledge-Based Systems Contextual sentiment embeddings via bi-directional GRU language,” ELSEVIER, vol. 235, 2022.

D. R. I. M. Setiadi, D. Marutho, and N. A. Setiyanto, “Comprehensive Exploration of Machine and Deep Learning Classification Methods for Aspect-Based Sentiment Analysis with Latent Dirichlet Allocation Topic Modeling,” J. Futur. Artif. Intell. Technol., vol. 1, no. 1, pp. 12–22, 2024, doi: 10.62411/faith.2024-3.

G. R. Narayanaswamy and R. Citation, “Exploiting BERT and RoBERTa to Improve Performance for Aspect Based Sentiment Analysis Gagan Reddy Narayanaswamy,” 2021, doi: 10.21427/3w9n-we77.

M. H. Phan and P. Ogunbona, “Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis,” pp. 3211–3220, 2020, doi: 10.18653/v1/2020.acl-main.293.

A. Musa, F. M. Adam, U. Ibrahim, and A. Y. Zandam, “HauBERT : A Transformer Model for Aspect-Based Sentiment Analysis of Hausa-Language Movie Reviews †,” pp. 1–18, 2025, doi: https://doi.org/10.3390/engproc2025087043.

E. Yulianti and N. K. Nissa, “ABSA of Indonesian customer reviews using IndoBERT : single- sentence and sentence-pair classification approaches,” vol. 13, no. 5, pp. 3579–3589, 2024, doi: 10.11591/eei.v13i5.8032.

D. A. Anggoro and N. D. Kurnia, “Comparison of Accuracy Level of Support Vector Machine ( SVM ) and K-Nearest Neighbors ( KNN ) Algorithms in Predicting Heart Disease,” vol. 8, no. 5, 2020, doi: https://doi.org/10.30534/ijeter/2020/32852020.

S. Shabani, S. Samadianfard, M. T. Sattari, and A. Mosavi, “Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines ; Comparative Analysis,” Atmosphere (Basel)., vol. 11, no. 66, 2020, doi: 10.3390/atmos11010066.

S. P. Sharma, L. Singh, and R. Tiwari, “Original Research Article Prediction of customer review ’ s helpfulness based on sentences encoding using CNN-BiGRU model,” vol. 6, no. 3, 2023, doi: 10.32629/jai.v6i3.699.

G. D. Aniello, M. Gaeta, and I. La, KnowMIS ‑ ABSA : an overview and a reference model for applications of sentiment analysis and aspect ‑ based sentiment analysis, vol. 55, no. 7. Springer Netherlands, 2022, doi: https://doi.org/10.1007/s10462-021-10134-9

J. Ouyang, Z. Yang, S. Liang, B. Wang, Y. Wang, and X. Li, “Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations,” in The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24) Aspect-Based, 2024, pp. 18842–18850, doi: https://doi.org/10.1609/aaai.v38i17.29849.

A. Khan, “RNN-LSTM-GRU based language transformation,” Soft Comput., vol. 0123456789, 2019, doi: 10.1007/s00500-019-04281-z.

F. M. Shiri, T. Perumal, and N. Mustapha, “A Comprehensive Overview and Comparative Analysis on Deep Learning Models,” no. Ml, 2024, doi: 10.32604/jai.2024.054314.

N. N. Cnn, L. Short, and T. Memory, “Sentiment Analysis on Twitter Data by Using Convolutional,” Springer, no. 0123456789, 2021, doi: 10.1007/s11277-021-08580-3.

A. Shewalkar, D. Nyavanandi, and S. A. Ludwig, “Performance Evaluation Of Deep Neural Networks Applied To Speech Recognition : Rnn , Lstm And Gru,” vol. 9, no. 4, pp. 235–245, 2019, doi: 10.2478/jaiscr-2019-0006.

B. Bilstm, S. Munawar, N. Javaid, Z. A. Khan, and N. I. Chaudhary, “Electricity Theft Detection in Smart Grids Using a Hybrid,” Sensors, vol. 22, 2022, doi: https://doi.org/10.3390/s22207818.

P. He, H. Qi, S. Wang, and J. Cang, “applied sciences Cross-Modal Sentiment Analysis of Text and Video Based on Bi-GRU Cyclic Network and Correlation Enhancement,” Appl. Sci., vol. 13, 2023, doi: https://doi.org/10.3390/app13137489.

A. Z. Arrayyan, H. Setiawan, and K. T. Putra, “Naive Bayes for Diabetes Prediction : Developing a Classification Model for Risk Identification in Specific Populations,” vol. 27, no. 1, pp. 28–36, 2024, doi: https://doi.org/10.18196/st.v27i1.21008.

I. Wickramasinghe, “Naive Bayes : applications , variations and vulnerabilities : a review of literature with code snippets for implementation,” Soft Comput., no. 1989, 2020, doi: 10.1007/s00500-020-05297-6.

E. Setiawan, F. Ferry, J. Santoso, S. Sumpeno, K. Fujisawa, and M. Purnomo, “Bidirectional GRU for Targeted Aspect-Based Sentiment Analysis Based on Character-Enhanced Token-Embedding and Multi-Level Attention,” Int. J. Intell. Eng. Syst., vol. 13, no. 5, pp. 392–407, Oct. 2020, doi: 10.22266/ijies2020.1031.35.




DOI: http://dx.doi.org/10.30829/zero.v9i2.25754

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Publisher :
Department of Mathematics
Faculty of Science and Technology
Universitas Islam Negeri Sumatera Utara Medan
📱 WhatsApp:085270009767 (Admin Official)
SINTA 2 Google Scholar CrossRef Garuda DOAJ