STOCK SENTIMENT ANALYSIS FROM NEWS HEADLINES BY USING ENSEMBLE BOOSTING ALGORITHMS
Abstract
Stock sentiment analysis using machine learning techniques is an active area of research and development. Through these techniques, it is possible to analyze whether news headlines are likely to have a positive or negative sentiment, which is considered a potential factor influencing stock price movements. The analysis in this study is based on factors such as company-related news headlines and their publication dates. This study aims to introduce a stock sentiment analysis methodology based on Ensemble Boosting Algorithms. The dataset used consists of 3,897 unique news headlines from various companies. In addition to Ensemble Boosting Algorithms, widely used classification algorithms were also trained and compared. The results demonstrate that the proposed models achieved successful performance in sentiment classification.
Keywords
References
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Details
Primary Language
English
Subjects
Finance
Journal Section
Research Article
Publication Date
February 26, 2026
Submission Date
May 8, 2025
Acceptance Date
July 15, 2025
Published in Issue
Year 2026 Volume: 27 Number: 1