IDEAS home Printed from https://ideas.repec.org/a/aag/wpaper/v26y2022i2p116-152.html
   My bibliography  Save this article

A Comprehensive Review of Stock Price Prediction Using Text Mining

Author

Listed:
  • Maede TajMazinani

    (Department of Finance and Insurance, University of Tehran)

  • Hosein Hassani

    (Research institute for energy management and planning, University of Tehran)

  • Reza Raei

    (Department of Finance and Insurance, University of Tehran)

Abstract

Purpose- In various studies, the sentiment analysis identifies as an essential part of stock price behavior prediction. The availability of news, social media networks, and the rapid development of natural language processing methods resulted in better forecasting performance. However, there is a lack of a comprehensive framework and review paper to address the advantages and challenges of this very timely topic. Design/methodology/approach- This paper aims to promote the existing literature in this field by focusing on different aspects of previous studies and presenting an explicit picture of their components. We, furthermore, compare each system with the rest and identify their main differentiating factors. This paper summarized and systematized studies that seek to predict stock prices based on text mining and sentiment analysis in a systematic review paper. Findings- It discussed the developments made during recent years and addressed the existing gap in this field to the research community.

Suggested Citation

  • Maede TajMazinani & Hosein Hassani & Reza Raei, 2022. "A Comprehensive Review of Stock Price Prediction Using Text Mining," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(2), pages 116-152, June.
    • Handle: RePEc:aag:wpaper:v:26:y:2022:i:2:p:116-152
    as

    Download full text from publisher

    File URL: https://iads.site/a-comprehensive-review-of-stock-price-prediction-using-text-mining/
    Download Restriction: no
    ---><---

    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:aag:wpaper:v:26:y:2022:i:2:p:116-152. 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: Vincent Pan (email available below). General contact details of provider: https://edirc.repec.org/data/dfasitw.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.