IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-124-1_51.html

Research Status of the Usefulness of Online Comments Based on Deep Learning

In: Proceedings of the 2022 3rd International Conference on Big Data Economy and Information Management (BDEIM 2022)

Author

Listed:
  • Erwen Wu

    (Zhejiang University of Finance and Economics, College Of Data Science)

Abstract

Nowadays, online comments posted to the Internet or electronics technology enables people or company to extract useful information, such as electronics plat users who have purchased products. Therefore, it is obvious that online comments are essential and useful so how to find the usefulness of online comments starts to become the research aim of many researchers. This paper summarizes literature in the field of Deep Learning on the usefulness of online comments. It is concluded that the Deep Learning model has high accuracy. Through these models, researchers can find the real situation from lots of online comments more accurately and easier and even can predict further behavior through these online comments. This paper is beneficial for people who have an interest in studying the application of Deep Learning on the usefulness of online comments to know the current development of this field preliminarily and find better models to improve accuracy.

Suggested Citation

  • Erwen Wu, 2023. "Research Status of the Usefulness of Online Comments Based on Deep Learning," Advances in Economics, Business and Management Research, in: Seifedine Kadry & Yingchen Yan & Junjie Xia (ed.), Proceedings of the 2022 3rd International Conference on Big Data Economy and Information Management (BDEIM 2022), pages 440-448, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-124-1_51
    DOI: 10.2991/978-94-6463-124-1_51
    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
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    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:spr:advbcp:978-94-6463-124-1_51. 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.