A Systematic Review on the Depression and Suicidal Thought Detection Using Machine Learning Based Classifiers

Social media is the platform that connects people around the globe, allowing them to freely express their feelings and thoughts. People share their opinion on different fields i.e., politics, sports, social-cultural issues etc. Many people express their physical and mental health also in the social platform to fight against depression and other mental conditions. However, in this online era, the people with mental health issues can be given attention, by identifying the depressed and suicidal thought patients. There are many advanced and emerging computing techniques to classify and grade depressed person and people with suicidal thought by analysing their social media posts and comments. Few research has been done by the researchers to classify depression using the trending technologies. In this paper a detailed survey has been carried out in this area. We also identified certain drawbacks and stumbling blocks of the existing works. The ethical side of things i.e., privacy consent, integrity of the content are also engaged in our paper. Based on this, we have explored some of the future research opportunities and scope of works, that can intensify and enhance the performance of the pre-explored research works in the detection of depression and suicidal thought.