Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter

Philip Resnik, William Armstrong, Leonardo Max Batista Claudino, Thang Nguyen, Viet-An Nguyen, Jordan L. Boyd-Graber

Abstract: Topic models can yield insight into how depressed and non-depressed individuals use language differently. In this paper, we explore the use of supervised topic models in the analysis of linguistic signal for detecting depression, providing promising results using several models.