Are You Depressed? Analyze User Utterances to Detect Depressive Emotions Using DistilBERT

Abstract

This paper introduces the Are u Depressed (AuD) model, which aims to detect depressive emotional intensity and classify detailed depressive symptoms expressed in user utterances. The study includes the creation of a BWS dataset using a tool for the Best-Worst Scaling annotation task and a DSM-5 dataset containing nine types of depression annotations based on major depressive disorder (MDD) episodes in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed model employs the DistilBERT model for both tasks and demonstrates superior performance compared to other machine learning and deep learning models. We suggest using our model for real-time depressive emotion detection tasks that demand speed and accuracy. Overall, the AuD model significantly advances the accurate detection of depressive emotions in user utterances.

Keywords

• depression intensity
• Best-Worst Scaling
• DSM-5 dataset
• DistilBERT
• attention