Abstract

Abstract: Facebook, Twitter, and Instagram, among other social media platforms, have irrevocably changed our world. People are more linked than ever before, and they have developed a digital character. Although social media has a number of appealing qualities, it also has a number of drawbacks. Recent research has found a link between excessive use of networking platforms that are social and greater depression. The aim of this research is to apply techniques of machine learning to identify a likely sad user of Twitter who is based on his or her behavior of network & tweets. The goal of this research is to apply techniques of machine learning to detect a possible unhappy Twitter user's tweets. We used variables collected from a user's behaviors within tweets to train & test Classifiers to differentiate that a person is depressed or not. On a scale of 0-100 percent, classification machine algorithms are used to train and classify it in different stages of depression. Also, data were collected in the form of tweets, which were categorized into whether the person who tweeted was depressed or not using Machine Learning classification algorithms. Predictive technique for early identification of depression or other diseases related to psychological in this way. The key contribution of this study is the investigation of a neighborhood of features and their consequences on finding levels of depression.