Detecting Level of Depression from Social Media Posts for the Low-resource Bengali Language

Abstract

Depression is a mental illness that suffers people in their thoughts and daily activities. In extreme cases, sometimes it leads to self-destruction or commit to suicide. Besides an individual, depression harms the victim's family, society, and working environment. Therefore, before physiological treatment, it is essential to identify depressed people first. As various social media platforms like Facebook overwhelm our everyday life, depressed people share their personal feelings and opinions through these platforms by sending posts or comments. We have detected many research work that experiment on those text messages in English and other highly-resourced languages. Limited works we have identified in low-resource languages like Bengali. In addition, most of these works deal with a binary classification problem. We classify the Bengali depression text into four classes: non-depressive, mild, moderate, and severe in this investigation. At first, we developed a depression dataset of 2,598 entries. Then, we apply pre-processing tasks, feature selection techniques, and three types of machine learning (ML) models: classical ML, deep-learning (DL), and transformer-based pre-trained models. The XLM-RoBERTa-based pre-trained model outperforms with 61.11% F1-score and 60.89% accuracy the existing works for the four levels of the depression-class classification problem. Our proposed machine learning-based automatic detection system can recognize the various stages of depression, from low to high. It may assist the psychologist or others in providing level-wise counseling to depressed people to return to their ordinary life.