Mental Health Disorder Indications Detection Based on Text Using NLP with EDA Augmentation Techniques

Machine Learning-Driven Mental Health Detection: A Comparative Analysis of Existing Techniques and Development of a Predictive Computational Model

A novel machine learning-driven predictive computational model based on DistilBERT for automated multi-class mental health identification from Reddit posts is proposed in this paper and indicates that transformer-based models are robust and scalable solutions for real-world mental health testing and decision-support sy...

Student Mental Health Risk Classification Using Random Forest and BERTopic: A Tabular and Text Analysis Approach

Student mental health has become a critical issue affecting academic performance and quality of life. Many previous studies have only used numerical data and have not explored textual data that reflects students' subjective conditions. This study develops a classification model using Random Forest on tabular data to pr...

Data Augmentation for Multi-Class Eating Disorders Text Classification

Assessing The Reliability Of Machine Learning Models In Classifying Depression Severity Using Facebook Text

It is demonstrated that machine learning algorithms can serve as effective tools for classifying Facebook text into varying levels of depression severity, but challenges such as class imbalance, limited data availability, and discrepancies between self-assessment results and online behavior indicate that further resear...

An Explainable Multi-Class Mental Health Classification Framework from Twitter Posts Using NLP and Machine Learning

Social media and digital platforms have changed the landscape of mental health discourses, making platforms such as Twitter key for individuals to discuss their mental health and find solace. Although these platforms provide important linguistic insights for different mental health disorders, most current computational...

Early Stage Depression Recognition Using Machine Learning and Natural Language Processing: A Hybrid PHQ-9 and Linguistic Feature-Based Screening Framework

Depression is a widespread and frequently under-diagnosed mental health condition, and delays in identification are associated with poorer long-term outcomes. This paper presents a hybrid screening framework that combines a validated questionnaire to the Patient Health Questionnaire-9 (PHQ-9), with linguistic feature e...