Burnout and Depression Detection Using Affective Word List Ratings

Haug, Sophie; Kurpicz-Briki, Mascha (2022). Burnout and Depression Detection Using Affective Word List Ratings Studies in health technology and informatics, 292, pp. 43-48. IOS Press 10.3233/SHTI220318

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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.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	School of Engineering and Computer Science > Institute for Data Applications and Security (IDAS) School of Engineering and Computer Science > Institute for Data Applications and Security (IDAS) > IDAS / Applied Machine Intelligence School of Engineering and Computer Science
Name:	Haug, Sophie and Kurpicz-Briki, Mascha0000-0001-5539-6370
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
ISSN:	1879-8365
ISBN:	978-1-64368-280-8
Series:	Studies in Health Technology and Informatics
Publisher:	IOS Press
Language:	English
Submitter:	Mascha Kurpicz-Briki
Date Deposited:	25 May 2022 13:06
Last Modified:	25 May 2022 13:06
Publisher DOI:	10.3233/SHTI220318
Related URLs:	Publication Organisation
ARBOR DOI:	10.24451/arbor.16998
URI:	https://arbor.bfh.ch/id/eprint/16998