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

Pratham Shah, Kush Desai, Mrudani Hada, Parth Parikh, Malav Champaneria, Dhyani Panchal, Mansi Tanna and Manan Shah ()
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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

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 6, No 1, 2018 pages

Abstract: 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.

Keywords: Sentiment; Stock market; Big data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-023-02214-6

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