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p-Index From 2021 - 2026
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All Journal Journal of Soft Computing Exploration
Agus Setiyo Budi Nugroho
Department of Computer Science, Politeknik Negeri Banjarmasin, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Web-based sentiment analysis of environmental issues on social media X: A comparison of svm and random forest Subandi; Agus Setiyo Budi Nugroho; Muhammad Hidayat
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.55

Abstract

This study aims to compare the performance of Support Vector Machine (SVM) and Random Forest (RF) in classifying public sentiment toward environmental issues on social media X (formerly Twitter) and to develop a web-based system for sentiment monitoring and visualization. A total of 47,245 tweets from 2021–2025 were collected using 24 environmental keywords. The data were processed through text cleaning, tokenization, stopword removal, and stemming. Sentiment labeling was performed automatically using a lexicon-based approach with the InSet dictionary, resulting in positive, negative, and neutral classes. After filtering, 13,063 tweets were used for model training. Classification employed TF-IDF features and 5-fold cross-validation. The results indicate that SVM outperformed RF with an accuracy of 83%, compared to 81%. Both models performed well in identifying sentiment polarity, although challenges remain in classifying neutral sentiment. The novelty of this study lies in integrating lexicon-based labeling with machine learning and implementing it in a web-based system for automated analysis and visualization. Practically, this system supports stakeholders in monitoring public opinion and enables data-driven decision-making in environmental policy and management.
Co-Authors Muhammad Hidayat Subandi