The Multimodal Emotion Information Analysis of E-Commerce Online Pricing in Electronic Word of Mouth

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.

Optimizing E-commerce Inventory to prevent Stock Outs using the Random Forest Algorithm Approach

“…In comparison, Neural Networks (NN) have been extensively used in various applications, including e-commerce. For example, a Q-learning algorithm model based on NN has been proposed for dynamic pricing problems in e-commerce product lines, demonstrating the applicability of NN in addressing e-commerce challenges [41]. Moreover, a neural network algorithm has been utilized for in-depth research and analysis on the sales dynamics prediction of virtual community knowledge sharing in cross-border e-commerce, highlighting the role of NN in predicting e-commerce sales dynamics [42].…”

Exploring investors' willingness to use robo-advisors: mediating role of emotional response

“…Prior research indicates that behavioral intention may not always accurately predict the actual use (Chang et al, 2021c;Laukkanen, 2016), which may also depend on other influencing factors such as trust and risk perception (Arruda Filho et al, 2022). Besides, several other critical motivational factors also impact the successful adoption of technologies, particularly for fintech platforms (Hanafizadeh et al, 2014a, b), for instance, users' emotions (Chen et al, 2022;Du et al, 2022;Hung et al, 2021). Therefore, it is necessary to propose a comprehensive model based on users' perceptions, emotions and behavioral intentions to better explain the essential motivation of robo-advisors adoption by users.…”