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A comprehensive review on sentiment analysis of social/web media big data for stock market prediction

Author

Listed:
  • Pratham Shah

    (Indus University)

  • Kush Desai

    (The LNM Institute of Information and Technology)

  • Mrudani Hada

    (Nirma University)

  • Parth Parikh

    (Nirma University)

  • Malav Champaneria

    (CHARUSAT)

  • Dhyani Panchal

    (CHARUSAT)

  • Mansi Tanna

    (Devangpatel Institute of Technology Research (DEPSTAR) Chandubhai S Patel Institute of Technology, CHARUSAT)

  • Manan Shah

    (Pandit Deendayal Energy University)

Abstract

It is generally known that public opinion and stock market dynamics are inextricably linked. With the growth of social and web-based media, online platforms have emerged as a key gauge of public mood. This digital environment produces a lot of data quickly. This extensive dataset's analysis offers priceless insights into the general public's perception, which in turn might influence market performance. The vast array of approaches for efficiently processing the sizable amount of data originating from social and web-based media are reviewed in detail in this study. Additionally, it looks at studies exploring the integration of big data analytics and sentiment insights for more accurate market predictions, as well as studies studying the prediction of stock market trends using sentiment analysis.

Suggested Citation

  • Pratham Shah & Kush Desai & Mrudani Hada & Parth Parikh & Malav Champaneria & Dhyani Panchal & Mansi Tanna & Manan Shah, 2024. "A comprehensive review on sentiment analysis of social/web media big data for stock market prediction," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(6), pages 2011-2018, June.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:6:d:10.1007_s13198-023-02214-6
    DOI: 10.1007/s13198-023-02214-6
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    References listed on IDEAS

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    1. Jai Prakash Verma & Sudeep Tanwar & Sanjay Garg & Ishit Gandhi & Nikita H. Bachani, 2019. "Evaluation of Pattern Based Customized Approach for Stock Market Trend Prediction With Big Data and Machine Learning Techniques," International Journal of Business Analytics (IJBAN), IGI Global Scientific Publishing, vol. 6(3), pages 1-15, July.
    2. Sun, Andrew & Lachanski, Michael & Fabozzi, Frank J., 2016. "Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 272-281.
    3. Yonghong Jiang & Bin Mo & He Nie, 2018. "Does investor sentiment dynamically impact stock returns from different investor horizons? Evidence from the US stock market using a multi-scale method," Applied Economics Letters, Taylor & Francis Journals, vol. 25(7), pages 472-476, April.
    4. V. P. Ramesh & Priyanga Baskaran & Aarthika Krishnamoorthy & Divya Damodaran & Preethi Sadasivam, 2019. "Back propagation neural network based big data analytics for a stock market challenge," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(14), pages 3622-3642, July.
    5. Panos Kanavos & Anna-Maria Fontrier & Jennifer Gill & Olina Efthymiadou, 2020. "Does external reference pricing deliver what it promises? Evidence on its impact at national level," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(1), pages 129-151, February.
    6. Maria Ward Otoo, 1999. "Consumer sentiment and the stock market," Finance and Economics Discussion Series 1999-60, Board of Governors of the Federal Reserve System (U.S.).
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