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All Journal EMITTER International Journal of Engineering Technology
Ode Andi Alamsyah
Universitas Logistik dan Bisnis International
Computer Science & IT

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Particle Swarm Optimization–Tuned Random Forest for Multi-Class Mental Health Sentiment Classification from Textual Data Supriady; Ode Andi Alamsyah; Nesya Salma Ramadhani; Syafrial Fachri Pane
EMITTER International Journal of Engineering Technology Vol 14 No 1 (2026)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v14i1.1056

Abstract

Mental health sentiment classification from textual data has attracted increasing attention as a computational approach to support large-scale psychological assessment; however, multi-class classification remains challenging due to noisy text, class imbalance, and semantic overlap among categories. This study proposes and evaluates a machine learning framework for seven-class mental health sentiment classification that integrates enhanced text preprocessing with lemmatization, data augmentation via back-translation, TF-IDF feature extraction, and systematic model evaluation across multiple classifiers, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, K-Nearest Neighbors, AdaBoost, and XGBoost, under three hyperparameter tuning strategies: Grid Search, Random Search, and Particle Swarm Optimization (PSO). Experimental results indicate that ensemble-based models consistently outperform single classifiers, with the PSO-optimized Random Forest achieving the best numerical performance, attaining an accuracy of 0.933, a macro F1-score of 0.923, and a ROC AUC of 0.989, demonstrating strong generalization and balanced class-level performance despite dataset imbalance. These findings confirm that the combination of robust preprocessing and metaheuristic-based hyperparameter optimization significantly enhances multi-class mental health sentiment classification and supports its potential use as a scalable decision-support tool for large-scale mental health screening, while not intended to replace clinical diagnosis.
Co-Authors Nesya Salma Ramadhani Supriady Syafrial Fachri Pane