Article: Enhancing retail decision-making accuracy through deep learning-based consumer sentiment simulation modelling Journal: International Journal of Simulation and Process Modelling (IJSPM) 2026 Vol.23 No.1 pp.24 - 33 Abstract: Confronted with the challenge that traditional retail decision systems struggle to quantify the impact of consumer sentiment, this paper proposes an agent-based simulation framework powered by a deep learning model integrating bidirectional encoder representations from transformers with a bidirectional long short-term memory network. This approach constructs an end-to-end consumer sentiment simulation system through the fusion of multimodal data, including textual reviews and behavioural sequences. Experiments on the publicly available Amazon review dataset demonstrate that this model achieves a sentiment recognition accuracy of 92.7%, representing a 15.3% improvement over traditional long short-term memory models. By systematically integrating fine-grained sentiment dimensions into the decision-making process, the system enabled a product recommendation conversion rate increase of 22.1% and an inventory turnover rate optimisation of 18.6%. The results robustly validate that the proposed sentiment simulation framework significantly enhances the precision and intelligence of retail decision-making. Inderscience Publishers - linking academia, business and industry through research

Title: Enhancing retail decision-making accuracy through deep learning-based consumer sentiment simulation modelling

Authors: Haoyang Qin; Enzhi Liu; Jiaxin Wang

Addresses: College of Economics and Management, Yantai Nanshan University, Yantai 265713, China ' School of Finance and Economics, University of Sanya, Sanya 572022, China ' College of Economics and Management, Yantai Nanshan University, Yantai 265713, China

Abstract: Confronted with the challenge that traditional retail decision systems struggle to quantify the impact of consumer sentiment, this paper proposes an agent-based simulation framework powered by a deep learning model integrating bidirectional encoder representations from transformers with a bidirectional long short-term memory network. This approach constructs an end-to-end consumer sentiment simulation system through the fusion of multimodal data, including textual reviews and behavioural sequences. Experiments on the publicly available Amazon review dataset demonstrate that this model achieves a sentiment recognition accuracy of 92.7%, representing a 15.3% improvement over traditional long short-term memory models. By systematically integrating fine-grained sentiment dimensions into the decision-making process, the system enabled a product recommendation conversion rate increase of 22.1% and an inventory turnover rate optimisation of 18.6%. The results robustly validate that the proposed sentiment simulation framework significantly enhances the precision and intelligence of retail decision-making.

Keywords: consumer sentiment simulation; retail decision optimisation; deep learning; multimodal data fusion; BERT-BILSTM model.

DOI: 10.1504/IJSPM.2026.152095

International Journal of Simulation and Process Modelling, 2026 Vol.23 No.1, pp.24 - 33

Received: 14 Oct 2025
Accepted: 24 Nov 2025
Published online: 06 Mar 2026 *

Title: Enhancing retail decision-making accuracy through deep learning-based consumer sentiment simulation modelling

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