{"title":"电子口碑下电子商务网上定价的多模式情感信息分析","authors":"Jinyu Chen, Ziqi Zhong, Qindi Feng, Lei Liu","doi":"10.4018/jgim.315322","DOIUrl":null,"url":null,"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.","PeriodicalId":46306,"journal":{"name":"Journal of Global Information Management","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The Multimodal Emotion Information Analysis of E-Commerce Online Pricing in Electronic Word of Mouth\",\"authors\":\"Jinyu Chen, Ziqi Zhong, Qindi Feng, Lei Liu\",\"doi\":\"10.4018/jgim.315322\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":46306,\"journal\":{\"name\":\"Journal of Global Information Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2022-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Global Information Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.4018/jgim.315322\",\"RegionNum\":3,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Global Information Management","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.4018/jgim.315322","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
The Multimodal Emotion Information Analysis of E-Commerce Online Pricing in Electronic Word of Mouth
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.
期刊介绍:
Authors are encouraged to submit manuscripts that are consistent to the following submission themes: (a) Cross-National Studies. These need not be cross-culture per se. These studies lead to understanding of IT as it leaves one nation and is built/bought/used in another. Generally, these studies bring to light transferability issues and they challenge if practices in one nation transfer. (b) Cross-Cultural Studies. These need not be cross-nation. Cultures could be across regions that share a similar culture. They can also be within nations. These studies lead to understanding of IT as it leaves one culture and is built/bought/used in another. Generally, these studies bring to light transferability issues and they challenge if practices in one culture transfer.