IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/125409.html
   My bibliography  Save this paper

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

Author

Listed:
  • Chen, Jinyu
  • Zhong, Ziqi
  • Feng, Qindi
  • Liu, Lei

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.

Suggested Citation

  • Chen, Jinyu & Zhong, Ziqi & Feng, Qindi & Liu, Lei, 2022. "The multimodal emotion information analysis of e-commerce online pricing in electronic word of mouth," LSE Research Online Documents on Economics 125409, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:125409
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/125409/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Keller, Alisa & Vogelsang, Mila & Totzek, Dirk, 2022. "How displaying price discounts can mitigate negative customer reactions to dynamic pricing," Journal of Business Research, Elsevier, vol. 148(C), pages 277-291.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yan Guo & Jiajun Lin & Weiqing Zhuang, 2024. "An Evolutionary Game-Based Regulatory Path for Algorithmic Price Discrimination in E-Commerce Platforms," Mathematics, MDPI, vol. 12(17), pages 1-30, September.
    2. Yilin Liang & Yuping Hu & Dongjun Luo & Qi Zhu & Qingxuan Chen & Chunmei Wang, 2023. "Distributed Dynamic Pricing Strategy Based on Deep Reinforcement Learning Approach in a Presale Mechanism," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    3. Jianjun Li & Xiaodi Xu & Yu Yang, 2023. "Research on the Regulation of Algorithmic Price Discrimination Behaviour of E-Commerce Platform Based on Tripartite Evolutionary Game," Sustainability, MDPI, vol. 15(10), pages 1-19, May.

    More about this item

    Keywords

    dynamic pricing; E-commerce; emotion recognition; neural network; Q-learning algorithm;
    All these keywords.

    JEL classification:

    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • J50 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:125409. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.