The multimodal emotion information analysis of e-commerce online pricing in electronic word of mouth

Jinyu Chen, Ziqi Zhong, Qindi Feng and Lei Liu

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: E-commerce has developed rapidly, and product promotion refers to how e-commerce promotes consumers' consumption activities. The demand and computational complexity in the decision-making process are urgent problems to be solved to optimize dynamic pricing decisions of the e-commerce product lines. Therefore, a Q-learning algorithm model based on the neural network is proposed on the premise of multimodal emotion information recognition and analysis, and the dynamic pricing problem of the product line is studied. The results show that a multi-modal fusion model is established through the multi-modal fusion of speech emotion recognition and image emotion recognition to classify consumers' emotions. Then, they are used as auxiliary materials for understanding and analyzing the market demand. The long short-term memory (LSTM) classifier performs excellent image feature extraction. The accuracy rate is 3.92%-6.74% higher than that of other similar classifiers, and the accuracy rate of the image single-feature optimal model is 9.32% higher than that of the speech single-feature model.

Keywords: dynamic pricing; E-commerce; emotion recognition; neural network; Q-learning algorithm (search for similar items in EconPapers)
JEL-codes: J50 L81 (search for similar items in EconPapers)
Date: 2022-12-31
New Economics Papers: this item is included in nep-big and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Journal of Global Information Management, 31, December, 2022, 30(11). ISSN: 1062-7375

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