DETECTING EMOTIONAL STATE OF DEPRESSION IN SOCIAL MEDIA POSTS USING LOGISTIC REGRESSION-RECURSIVE FEATURE ELIMINATION

Abstract

Depression detection through social media has garnered widespread attention due to its potential for early intervention in mental health issues. This study aims to detect depressive users based on their content shared on social media using machine learning techniques. Given the complexity and diversity of depressive text, existing research still falls short in exploring comprehensive feature extraction techniques. To address this challenge, this study proposes an integrated framework for detecting depressive tendencies through multi-dimensional feature extraction and selection techniques. The proposed approach combines TF-IDF with N-grams, DistilBERT embeddings, and SentiWordNet to capture linguistic, semantic, and emotional features. Additionally, logistic regression-based recursive feature elimination (LR-RFE) is employed to optimize high-dimensional feature sets by reducing redundancy and emphasizing key indicators.Experiments conducted on the CLEF eRisk dataset revealed varying levels of effectiveness across individual feature extraction methods. Notably, multi-feature integration significantly enhanced classification performance, achieving an accuracy of 80.8% and an F1 score of 80.54% with the combined feature set. Feature selection further improved model efficiency and performance. These findings contribute to advancing automated depression detection and lay a foundation for developing scalable and interpretable machine learning models for mental health assessment.