Measuring Investor Sentiment in Financial Discourse: How Different Approaches Shape Market Signals
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26510%2F26%3A0200014" target="_blank" >RIV/00216305:26510/26:0200014 - isvavai.cz</a>
Result on the web
<a href="https://ecocyb.ase.ro/nr2025_4/5_ZuzanaJankova_NikolozKavelashvili.pdf" target="_blank" >https://ecocyb.ase.ro/nr2025_4/5_ZuzanaJankova_NikolozKavelashvili.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.24818/18423264/59.4.25.05" target="_blank" >10.24818/18423264/59.4.25.05</a>
Alternative languages
Result language
angličtina
Original language name
Measuring Investor Sentiment in Financial Discourse: How Different Approaches Shape Market Signals
Original language description
Stock prices are shaped not only by fundamental data but also by investor sentiment, which often deviates from rational decision-making. Given the vast volume of financial texts published by both professional and amateur investors—especially on online financial platforms—sentiment analysis in such unstructured data is essential to understanding their impact on market movements. This study examines the interplay between text data and stock market movements, highlighting the critical role of sentiment extracted from financial news and online news. Existing research has largely relied on general-purpose lexicons or uniform classification techniques, which limits the accuracy of sentiment analysis in financial contexts. To address this gap, we propose a hybrid framework that integrates domain-specific lexicons with advanced machine learning classifiers to improve sentiment extraction from unstructured financial text. Our approach evaluates the impact of lexicon selection on sentiment scores and examines the relationship between classifier choice and prediction accuracy. By improving sentiment analysis methodologies, our findings contribute to the development of more robust stock market forecasting models, strengthen decision-making processes for investors, and increase market efficiency.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50206 - Finance
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Economic Computation and Economic Cybernetics Studies and Research
ISSN
0424-267X
e-ISSN
1842-3264
Volume of the periodical
59
Issue of the periodical within the volume
4
Country of publishing house
RO - ROMANIA
Number of pages
19
Pages from-to
79-97
UT code for WoS article
001651970500005
EID of the result in the Scopus database
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