AN ENHANCED ENSEMBLE LEARNING FRAMEWORK FOR STRESS DETECTION IN SOCIAL MEDIA TEXT USING LINGUISTIC ANALYSIS

Abstract

This research presents a novel approach for the purpose of detecting stress using enhanced ensemble learning techniques combined with linguistic analysis, through social media text. With the growing issue of mental health concerns expressed online, accurate and timely detection of stress indicators is very important. The proposed model integrates multiple machine learning classifiers and improves detection accuracy by utilizing linguistic features such as syntax, semantics, and sentiment cues. A comprehensive dataset of SubReddit posts was used to train and evaluate the model. Results demonstrated that the ensemble approach outperformed individual classifiers and it is offering a promising tool for early stress detection and mental health monitoring in digital era.

Keywords

 Ensemble Learning, social media, Stress detection, Linguistic analysis