Using Different Machine Learning Algorithms, Depression is Detected
Author : Hatoon AlSagri
Abstract :
As more and more individuals experience it, the problem of depression in our society is taking on a greater significance. It's a crippling condition that may afflict people of all ages, and while some people may be aware that they have it, others may not. People have begun to use social media as a venue to document their emotional states, and research has been done using machine learning algorithms to identify depression by examining social media posts. It is possible to tell whether a person is depressed or not by examining the data that is available on social media (Twitter). To categorise the data and separate depressive from non-depressive posts, machine learning methods are applied. The suggested system identifies depression using Twitter data and a variety of techniques, including XGBoost, SVM, Logistic Regression, and Random Forest Classifier. The XGBoost algorithm was found to have the best accuracy level when the results were compared based on the highest accuracy level. Real-time classification is introduced with a user interface.
Keywords :
Depression, Twitter, XGBoost
