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All Journal PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Fadri, Agil Irman
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Sentiment Analysis of Public Opinion on the Gaza Conflict Using Machine Learning Fadri, Agil Irman; Jelita, Nur Futri Ayu; Bagaskara, Diamond Dimas; Zahra, Raudiatul
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 2: PREDATECS January 2026
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i2.2088

Abstract

The 2023 escalation of the Gaza conflict triggered widespread public discourse on the X platform, highlighting the importance of sentiment analysis for understanding public opinion on global geopolitical issues. While sentiment analysis has been widely applied to social media data, comparative evaluations of machine learning models on conflict-related datasets remain limited. This study analyzes public sentiment toward the Gaza conflict by comparing the performance of Multi-Layer Perceptron, XGBoost, and Logistic Regression models. A dataset of 2,175 tweets was processed using standard text preprocessing techniques and TF-IDF feature extraction. Model performance was evaluated using multiple train-test split scenarios. The results indicate that Logistic Regression consistently outperformed the other models, achieving the highest accuracy of 73.17% with an 80:20 data split. These findings suggest that simpler linear models may perform more robustly and efficiently than more complex approaches when applied to high-dimensional, noisy social media text data. This study provides practical insights into model selection for sentiment analysis of conflict-related discussions on social media platforms.
Co-Authors Bagaskara, Diamond Dimas Jelita, Nur Futri Ayu Zahra, Raudiatul