Multi-Class Multi-Level Classification of Mental Health Disorders Based on Textual Data from Social Media

Abstract

Mental health disorders pose a significant global public health challenge. Social media data provides insights into these conditions.
Analysing text can help identify indications of mental health disorders through text-based analysis. However, despite the large
number of studies on the analysis of mental health disorders, the predominant algorithm in the existing literature is the Multi-Class
Single-Level (MCSL) classification algorithm, which is often used for simple classification tasks involving a limited number of classes.
Typically, these classes are binary, representing either an unhealthy or a healthy mental state. This paper uses English text data from Reddit to classify mental health disorders. The Multi-Class Multi-Level (MCML) classification algorithm was applied to perform detailed
classification and address the limitations of the research scope using several approaches, including machine learning, deep learning, and transfer learning approaches. Two different pre-processing scenarios were proposed to handle unstructured text data, one of the most challenging aspects of classifying text from social media. The results of the experiments show that the MCML classification algorithm successfully performs detailed classification and produces promising results for each classification level. The proposed pre-processing scenario influences the performance of each classifier and improves classification accuracy. The best accuracy results were obtained for the Robustly Optimised BERT Pre-training Approach (RoBERTa) classifier at level 1 and level 2 classifications, namely 0.98 and 0.85, respectively. Overall, the MCML classification algorithm is proven to be used as a benchmark for early detection of text-based mental health disorders.