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

Abstract

This study aims to build a classification model for the early screening of mental health disorders from social media text data using the CRISP-DM framework. The primary issue of data imbalance between categories was addressed using the Easy Data Augmentation (EDA) technique. Logistic Regression algorithm and TF-IDF feature extraction were used to classify six categories of mental conditions. Test results showed that the model with EDA experienced a slight decrease in global accuracy to 0.74 (compared to 0.76 without EDA) but successfully increased the Recall for the minority class, Mentalillness, significantly from 0.28 to 0.56. This improvement proves that EDA effectively enriches linguistic variation in limited data. The model has been validated by a psychologist and implemented into a web-based application as an indicative early detection tool, not a clinical medical diagnosis.