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Multi-class mental health classification on social media using ensemble transformer models and emoji sentiment analysis

data-mining-and-machine-learning
data-science
text-mining
neural-networks

Abstract

Social media platforms are increasingly utilized for emotional support, sparking interest in using social media text as a tool for detecting mental illnesses. This study aims to enable early intervention and support for individuals facing mental health challenges by analyzing their social media data. Specifically, it examines the potential of Reddit posts and comments to identify mental health conditions such as depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, and autism.

This study utilizes advanced NLP, including BERT and RoBERTa, and ensemble learning techniques to analyze Reddit posts for early detection of mental health disorders.

The proposed model achieves an F1-score of 0.83, demonstrating improved detection performance compared to prior methods. Findings highlight the significance of emoji integration in enhancing classification accuracy.

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