IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-97-7819-5_12.html
   My bibliography  Save this book chapter

Elevating Customer Satisfaction: AI Sentiment Analysis in Vietnamese E-Retail Landscape

In: Transforming Logistics in a Developing Nation

Author

Listed:
  • Tram Thi Bich Nguyen

    (Ho Chi Minh City Open University)

  • Khang Dinh Nguyen

    (Ho Chi Minh City University of Technology)

Abstract

In recent years, sentiment analysis has become increasingly prevalent across various sectors, including businesses and governmental bodies, facilitated by the widespread use of the Internet as a platform for collective sentiment expression. The emergence of Artificial Intelligence (AI) and Machine Learning (ML) has transformed sentiment analysis, enabling efficient analysis of vast volumes of customer feedback data. Vietnam's e-commerce market, one of Southeast Asia's fastest-growing, is poised for significant expansion, driven by a growing digital population and increasing internet penetration rates. With its favorable environment for e-commerce enterprises, Vietnam presents ample opportunities for growth. This study employs sentiment analysis to explore the role of customer feedback in enhancing the satisfaction of Vietnamese e-retailers, proposing improvement strategies for local sellers on popular e-marketplaces like Shopee and Lazada. Through sentiment analysis, our aim is to provide insights into various aspects such as product descriptions, perceived value, size guides, and delivery practices, thereby fostering business performance growth for Vietnamese e-retailers.

Suggested Citation

  • Tram Thi Bich Nguyen & Khang Dinh Nguyen, 2024. "Elevating Customer Satisfaction: AI Sentiment Analysis in Vietnamese E-Retail Landscape," Springer Books, in: Scott Douglas McDonald & Minh Duong Kim Ngo (ed.), Transforming Logistics in a Developing Nation, chapter 0, pages 347-369, Springer.
  • Handle: RePEc:spr:sprchp:978-981-97-7819-5_12
    DOI: 10.1007/978-981-97-7819-5_12
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:sprchp:978-981-97-7819-5_12. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.