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Burnout and Depression Detection Using Affective Word List Ratings
Sophie Haug, Mascha Kurpicz-Briki
43 - 48
10.3233/SHTI220318
Research Article
Studies in Health Technology and Informatics
Volume 292: Healthcare of the Future 2022
Abstract

Burnout syndrome and depression are prevalent mental health problems in many societies today. Most existing methods used in clinical intervention and research are based on inventories. Natural Language Processing (NLP) enables new possibilities to automatically evaluate text in the context of clinical Psychology. In this paper, we show how affective word list ratings can be used to differentiate between texts indicating depression or burnout, and a control group. In particular, we show that depression and burnout show statistically significantly higher arousal than the control group.

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