Prediction of Depression via Social Media and Ways to Provide Solution

Abstract

Depression is common and serious medical illness that negatively affects how individual feels, the way individual thinks and act. Depression affects people from all walks of life, no matter what their background. It can affect people of all ages as well. Usually we can see individuals expressing their feelings on social networking sites (SNS) like Facebook, twitter, You tube, Instagram through the posts, comments, likes, dislikes etc. Data of each individual's activity on SNS can be collected by crowdsourcing. By deep analyzing and understanding these collective data of an individual we can identify positive and negative feelings of an individual. Through this we can come up with the best way of providing solution to depressed individual to overcome mental illness. We can use Naive Bayes algorithm which is a machine learning algorithm, used to classify the depression level into different levels and it also provides doctor's location near to the identified depressed individual. Understanding the latest depression statistics could increase awareness about mental health. Recognizing how widespread it is could also help reduce the stigma- which might encourage more people to seek treatment. Main concern of this survey is to find depressed individuals and approach them with the positive entities. By encouraging the depressed minds with positive joy, happiness, and positive feelings we can help an individual to overcome their negative thoughts and depression.