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

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

Jinyu Chen (Southwest University of Science and Technology, China), Ziqi Zhong (London School of Economics and Political Science, UK), Qindi Feng (Gachon University, South Korea), and Lei Liu (Chuzhou University, China)
Copyright: © 2022 |Pages: 17
DOI: 10.4018/JGIM.315322
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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.
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1. Introduction

1.1 Research Background and Motivations

The development of science and technology is constantly changing people’s daily habits. E-commerce has become indispensable in people’s lives after its development in recent years (Carta et al., 2018). E-commerce is increasingly becoming the primary tool for selling goods to the mass market (Li et al., 2020). This has led to a growing interest in algorithms and techniques that can predict the future price of a product. They can build intelligent systems that provide affordable goods and services for people (Guan et al., 2020). Online e-commerce platforms provide sellers and consumers with convenient and inexpensive means to facilitate information sharing and docking trade-offs (Wu et al., 2020). The platform economy and e-commerce have become the fastest growing and most dynamic economic sectors in China and the world (Xu et al., 2021). Market competition is becoming increasingly fierce, and consumers’ purchasing choices are closely related to product brands. The positive role of brands is increasingly prominent (Leung et al., 2019). An online shopping platform is a unique network group organization that connects products and consumers’ purchasing channels. It has gradually become an important communication channel for online brand product sales, profoundly affecting consumers’ purchasing choices and brand building (Chen et al., 2020). Dynamic pricing can promote consumption, and the research on dynamic pricing of e-commerce products is necessary.

1.2 Research Objectives

The pricing strategy of e-commerce product lines must be in line with consumers’ demand and psychology to stand out in increasingly fierce market competition (Amanah and Harahap, 2018). Dynamic adjustment of prices in different scenarios can stimulate consumers to consume. However, the product’s reputation should be considered to promote consumer consumption and enhance their interest in long-term benefits. Therefore, the pricing of e-commerce product lines is critical. Besides, research on pricing strategies is a hot topic. The e-commerce pricing strategy is studied based on multimodal emotion recognition to optimize the pricing strategy of e-commerce. Compared with traditional e-commerce pricing, the proposed method dynamically prices products by studying the influence of consumers’ emotions on their consumption behaviors.

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2. Literature Review

At present, many scholars have studied the dynamic pricing of e-commerce products. Tseng et al. (2018) stated that online emotion analysis could be used to explore various possibilities, ranging from effects on product prices to impact on sales behavior and essential brand strategies (Tseng et al., 2018). Lv et al. (2020) argued that price discounts could significantly increase social e-commerce sales due to online consumer interactions (Lv et al. 2020). Liu et al. (2022) claimed that consumers increasingly demanded diversified online services. Many online service providers competed fiercely in providing online composite services to meet market demands (Liu et al., 2022). Moerth-Teo et al. (2021) pointed out that it is necessary to scientifically guide e-commerce to price products and improve the profits of service providers to meet consumers’ most extraordinary service demands (Moerth-Teo et al., 2021). Ali and Bhasin (2019) indicated that consumers’ perceptions of price and delivery quality greatly impacted subsequent shopping intentions. Therefore, the right price greatly influenced consumers’ repurchasing (Ali and Bhasin, 2019). Keller et al. (2022) studied the impact of price discounts on dynamic pricing. The results showed that dynamic pricing reduced consumers’ willingness to buy, but a large discount could reduce negative consumer sentiment (Keller et al., 2022). Kayikci et al. (2022) proposed a dynamic pricing model. The model used real-time Internet of Things sensor data to contribute significantly to merchants’ dynamic pricing at different stages of the sales season (Kayikci et al., 2022).

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