Early Detection of Depression from Social Media Data Using Machine Learning Algorithms

Depression is a very severe and grave mental disorder, which is affecting most of the population nowadays because of various reasons like stress at work, school, college, personal life, other diseases, etc. It is also called as Major depressive disorder. Though it is a very common disease, it is still a taboo to talk about depression in this world. People are reluctant to talk about this disease, thinking that people will take them like a crazy person. This reluctance can sometimes become very harmful for the patient, taking him to a point where he can't be treated back to normal. Early findings of the symptoms for. Various social networking platforms are used to allocate various data and routines with others. This can prove to be really helpful in understanding the mind of a depressed person. Machine learning algorithms have proved to be quite helpful in the past where researchers have worked on social media data to predict the number of people suffering from depression based on their early symptoms and social media activity. The Method of objective is to divide the approach into two parts - the first one is based on the time and writing patterns of the content and the second one is based on the linguistic clues, analysing the text or the tweet which has been shared. The objective is to help patients suffering from this disease in the early detection of the symptoms of depression which could prove beneficial to them and to their family too.