IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v12y2025i5d10.1007_s40745-024-00580-x.html
   My bibliography  Save this article

Sentiment-Based Hierarchical Deep Learning Framework Using Hybrid Optimization for Course Recommendation in E-learning

Author

Listed:
  • A. Madhavi

    (VNR Vignana Jyothi Institute of Engineering and Technology)

  • A. Nagesh

    (Mahatma Gandhi Institute of Technology)

  • A. Govardhan

    (Jawaharlal Nehru Technological University, UCEST
    RGUKT)

Abstract

Course recommendation (CD) is essential for success in a student’s educational journey. Due to the variations in student’s knowledge system, it might be difficult to select the course content from online educational platforms. This problem is overcome by introducing the Political Jellyfish search optimization (PJSO) based Hierarchical Deep Learning for Text (HDLTex) model for sentiment classification (SC) in CD. Here, the input data is taken from the E-khool database, which is subjected to the learner/course agglomerative matrix calculation. Then, the course is grouped by utilizing Bayesian Fuzzy clustering (BFC). When the query is given, bi-level matching is performed. The learner retrieves the preferred items after the best course group is found. Furthermore, course review data is applied to the tokenization process employing Bidirectional Encoder Representations from Transformers (BERT). Finally, the feature extraction is carried out and SC is performed by using HDLTex, which is trained by the proposed PJSO. Moreover, the PJSO is the incorporation of Political Optimizer (PO) and Jellyfish Search Optimization (JSO). The devised PJSO-based HDLTex has a superior assessment for maximum precision of 0.904, maximum recall of 0.915 and maximum F-Measure of 0.904 respectively.

Suggested Citation

  • A. Madhavi & A. Nagesh & A. Govardhan, 2025. "Sentiment-Based Hierarchical Deep Learning Framework Using Hybrid Optimization for Course Recommendation in E-learning," Annals of Data Science, Springer, vol. 12(5), pages 1661-1690, October.
    • Handle: RePEc:spr:aodasc:v:12:y:2025:i:5:d:10.1007_s40745-024-00580-x
    DOI: 10.1007/s40745-024-00580-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-024-00580-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-024-00580-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    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:aodasc:v:12:y:2025:i:5:d:10.1007_s40745-024-00580-x. 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.