A Decision Support System for Predicting Socially Depressed Users Using Bidirectional Encoders Representations from Transformers (BERT)

Depression is one of the leading causes of suicides in society. The youth of the 21st century are inclined towards social media for all their needs and expressions. Close friends can easily predict if someone is happy, sad, or depressed from a user’s daily social media activity like status uploads/shares/reposts/check-ins, etc. This activity can be analyzed in order to understand the pattern of mental health. Such data is easily available and if suspected, it can be reported to a Psychiatrist and Psychologist to prevent socially active depressed patients from taking any wrong decisions regarding their life thus providing a Decision Support System (DSS). Various natural language processing techniques have been used in order to detect depression but there is a need for a unified architecture that is based on contextual data and is bidirectional in nature. This can be achieved by using example be achieved by using the Google research project (BERT) Bidirectional Encoder Representations from Transformers.