International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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BIBLIOMETRIC ANALYSIS OF STOCK MARKET SENTIMENTS USING MACHINE LEARNING

Author(s) Soni, Nishant Kumar
Country India
Abstract Sentiment analysis has gained significant attention in financial markets for its potential to gauge market
sentiment and predict stock price movements. This bibliometric analysis explores the landscape of
existing research literature in sentiment analysis using machine-learning techniques in the stock market
domain. for this purpose, I, extract 572 research articles pertinent to the chosen field. These articles
were published in 368 journals between January 2012 to April 2024 and have been listed in the Scopus
database. By systematically reviewing relevant literature, this study aims to identify key trends,
research themes, influential authors, and publication outlets in the field. The paper employed a blend of
bibliometric and network analysis techniques using “R” and VOSviewer, such as co-citation analysis,
keyword co-occurrence analysis, and author co-citation analysis to uncover sentiment analysis's
intellectual structure and evolution in stock market research. The findings of this study provide valuable
insights for practitioners, researchers, and policymakers interested in understanding the advancements
and future directions of research in sentiment analysis of investors in the stock market.
Keywords Bibliometric Analysis, Sentiment Analysis, Stock Market, Financial Market, Machine Learning, and Natural Language Processing.
Field Business Administration
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-05
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.34634
Short DOI https://doi.org/g82hft
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