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.
Qin et al. (Thu,) studied this question.