Tweep: A System Development to Detect Depression in Twitter Posts

This paper presents a system development named Tweep that enables a consumer to analyze depression status using Machine Learning based on personal Twitter posts. In order for the consumer to curb mental illness, Tweep does not only analyze Twitter users' personal depression status, but also that of the people they follow on Twitter i.e. their 'following'. This project is the first work that practices a user-friendly interface system that analyzes depression status for public use. The system uses rule-based Vader Sentiment Analysis and two Machine Learning techniques namely Naive Bayes and Convolutional Neural Network. The output of the system is the percentage of the positive and negative posts of the Twitter users and of their followings.