Analyze Detection Depression In Social Media Twitter Using Bidirectional Encoder Representations from Transformers

Abstract

Human health is an essential part of the welfare of a country. Early detection of a disease is necessary to prevent it from spreading in an area. Social media is now a rapid and widespread development of information to provide convenience for the public to communicate. Depressed people have a variety of depressive symptoms from every human behaviour. Psychological doctors often conduct face-to-face interviews on commonly used diagnoses and statistical manual criteria for mental disorders. Depression is a mental disorder that typically appears in humans with the characteristics of depressed mood, loss of interest and pleasure, unstable body energy, and poor concentration. In conducting this research, the aim is to detect people who are depressed by using the Machine Learning-based BERT (Bidirectional Encoder Representations from Transformers) method. BERT can binarily classify text on social media, namely Twitter, which contains Depression detection. Based on the tests that have been carried out, the best accuracy value is 0.7176 or 71%.