Depression analysis on users of social network using machine learning algorithms

Abstract

Depression is a serious conditioned mental disorder that has significant effects on the quality of life of a person. Internet sources state that the number of people suffering from depression is getting increased day by day and it affects teenagers more than adults. Our project in this work is to find the status of a user's posts or comments which show depression mood or not, using different types of machine learning classification algorithms. The dataset is collected from users who share their day-to-day status on social networks. The dataset is preprocessed and tokenized to make it compatible to feed into different types of algorithms such as Naïve Bayes, Random Forest, Linear Regression, and Support Vector Machine. During the process, the accuracy level of each algorithm is compared and the algorithm with the highest accuracy has chosen as suitable to process further prediction.