AUTHOR=Obagbuwa Ibidun Christiana , Danster Samantha , Chibaya Onil Colin 

TITLE=Supervised machine learning models for depression sentiment analysis

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 6 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1230649

DOI=10.3389/frai.2023.1230649

ISSN=2624-8212

ABSTRACT=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.