Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/317620 
Year of Publication: 
2023
Citation: 
[Journal:] Journal of Business Economics and Management (JBEM) [ISSN:] 2029-4433 [Volume:] 24 [Issue:] 1 [Year:] 2023 [Pages:] 177-198
Publisher: 
Vilnius Gediminas Technical University, Vilnius
Abstract: 
The paper is aimed at a critical review of the literature dealing with text mining and sentiment analysis for stock market prediction. The aim of this work is to create a critical review of the literature, especially with regard to the latest findings of research articles in the selected topic strictly focused on stock markets represented by stock indices or stock titles. This requires examining and critically analyzing the methods used in the analysis of sentiment from textual data, with special regard to the possibility of generalization and transferability of research results. For this reason, an analytical approach is also used in working with the literature and a critical approach in its organization, especially for completeness, coherence, and consistency. Based on the selected criteria, 260 articles corresponding to the subject area are selected from the world databases of Web of Science and Scopus. These studies are graphically captured through bibliometric analysis. Subsequently, the selection of articles was narrowed to 49. The outputs are synthesized and the main findings and limits of the current state of research are highlighted with possible future directions of subsequent research.
Subjects: 
bibliometric analysis
financial market
literature review
sentiment analysis
stock market
text mining
JEL: 
C53
C63
G11
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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