Depression and anxiety detection through the closed-loop method using DASS-21

Abstract

The change of information and communication technology has brought many changes in daily life. The way humans interacting is changing. It is possible to express each form of communication directly and instantly. Social media has contributed data in size, diversity and capacity and quality. Based on it, the idea was to see and measure the tendency of depression and anxiety through social media using the Closed-Loop method using Facebook text mining posts. Through the stages of pre-processing including text extraction using the Naïve Bayes machine learning model for text classification, the early signs of depression and anxiety are measured using DASS-21 parameter. In total, 22,934 Facebook posts were contributed as training and learning data collected from July 2017 until July 2018. As a results, analysis and mapping of social demographics of users that are usually as a trigger of depression, and anxiety, such as grief, illness, household affairs, children education and others are available.