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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Tran, Oanh Thi
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Computer Science & IT Engineering

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A reinforcement learning paradigm for Vietnamese aspect-based sentiment analysis Bui, Viet The; Ngo, Linh Thuy; Tran, Oanh Thi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3375-3385

Abstract

This paper presents the task of aspect-based sentiment analysis (ABSA) that recognizes the sentiment polarity associated with each aspect of entities discussed in customers’ reviews, focusing on a low-resourced language, Vietnamese. Unlike conventional classification approaches, we leverage reinforcement learning (RL) techniques by formulating the task as a Markov decision process. This approach allows an RL agent to handle the hierarchical nature of ABSA, sequentially predicting entities, aspects, and sentiments by exploiting review features and previously predicted labels. The agent seeks to discover optimal policies by maximizing cumulative long-term rewards through accurate entity, aspect, and sentiment predictions. The experimental results on public Vietnamese datasets showed that the proposed approach yielded new state of the art (SOTA) results in both hotel and restaurant domains. Using the best model, we achieved an improvement of 1% to 3% in the F1 scores for detecting aspects and the corresponding sentiment polarity.
Predicting non-performing loans in Vietnam’s financial sector: a deep Q-learning approach Anh Do, Luyen; Viet Pham, Huong Thi; Duc Le, Thinh; Tran, Oanh Thi
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp700-709

Abstract

Non-performing loans (NPLs) prediction is a very important task in risk management of financial institutions. NPLs often lead to substantial losses when loans are not paid back on time. While traditional machine learning (ML) models have been conventionally exploited for credit risk assessment, they frequently face challenges with handling imbalanced data. To deal with this problem, this paper introduces a novel approach using deep reinforcement learning (DRL), specifically deep Q-learning, to enhance the prediction of NPLs. To verify the effectiveness of the method, we introduce a new dataset comprising 83,732 customer records (each described with 22 key features) from one of Vietnam's largest financial entities. Our method is compared with standard ML techniques such as random forest, decision tree, logistic regression, support vector machine, LightGBM, and XGBoost. Experimental results on this dataset demonstrate that deep Q-learning outperforms these traditional models in handling imbalanced data and boosting prediction accuracy. This research highlights the potential of DRL as a robust risk management tool, helping financial institutions make credit assessments more efficiently and reducing decision-making costs.
Co-Authors Anh Do, Luyen Bui, Viet The Duc Le, Thinh Ngo, Linh Thuy Viet Pham, Huong Thi