IDEAS home Printed from https://ideas.repec.org/a/sae/vision/v30y2026i3p360-377.html

Sentiment Analysis Using Machine Learning in Stock Market: A Bibliometric Visualization

Author

Listed:
  • Shivani
  • Bhag Singh Bodla

Abstract

Sentiments, opinions and emotions are considered as the basis for various kinds of analysis and anticipations. Sentiment analysis as a concept of generating the polarity from written, visual and verbal content is burgeoning in the advanced technological era. The article aims to carry out a bibliometric visualization study that reviews the current status, worldwide collaboration, evolution and potential emergence of machine learning-based sentiment analysis in the area of financial market along with several other themes. A corpus of 610 research publications from the Web of Science database during 2008–2022 is the base for depicting the contribution of eminent authors, journals, countries and research themes. Outcomes of the study reveal the wide scope of the ‘Sentiment Analysis’ theme in the existing research world along with providing a direction for future studies which may embed several advanced topics. The theme-related publications are segmented into four clusters where the ‘sentiment analysis and predictions using machine learning’ reflects the future emergence with existing growth. This concept has widely spread in India, China and the USA. Potential researchers can implement this concept in the stock market and financial predictions, sarcasm detection and behavioural mapping as major contributions to the firms’ effective decisions.

Suggested Citation

  • Shivani & Bhag Singh Bodla, 2026. "Sentiment Analysis Using Machine Learning in Stock Market: A Bibliometric Visualization," Vision, , vol. 30(3), pages 360-377, June.
    • Handle: RePEc:sae:vision:v:30:y:2026:i:3:p:360-377
    DOI: 10.1177/09722629231172580
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/09722629231172580
    Download Restriction: no
    File URL: https://libkey.io/10.1177/09722629231172580?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
    ---><---

    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:sae:vision:v:30:y:2026:i:3:p:360-377. 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: SAGE Publications (email available below). General contact details of provider: .

    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.