Supervised machine learning models for depression sentiment analysis - Obagbuwa, Ibidun Christiana; Danster, Samantha; Chibaya, Onil Colin (Frontiers in Artificial Intelligence, 2023)
Globally, the prevalence of mental health problems, especially depression, is at an all-time high. The goal of this paper is to predict the level of depression earlier in social media users' posts using machine learning models and sentiment analysis techniques. The datasets used in this work were derived from Twitter posts. The four models utilized are extreme gradient boost (XGB) Classifier, Random Forest, Logistic Regression, and support vector machine (SVM). SVM and Logistic Regression models delivered the most accurate results on the provided datasets, with Logistic Regression slightly exceeding SVM. The logistic regression model, however, takes the least time to execute.