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Burnout and Depression Detection Using Affective Word List Ratings
Sophie Haug1, Mascha Kurpicz-Briki1
1Applied Machine Intelligence, Bern University of Applied Sciences, Biel, Switzerland.
Studies in Health Technology and Informatics
|May 16, 2022
Summary
Burnout syndrome and depression are common mental health issues. This study shows that analyzing text using affective word lists can distinguish these conditions from a control group, noting higher arousal in burnout and depression.
Area of Science:
- Clinical Psychology
- Computational Linguistics
- Affective Computing
Background:
- Burnout syndrome and depression are significant global mental health concerns.
- Current diagnostic and research methods predominantly rely on self-report inventories.
- Natural Language Processing (NLP) offers novel avenues for automated text analysis in clinical psychology.
Purpose of the Study:
- To investigate the efficacy of affective word list ratings in differentiating texts related to depression and burnout from control texts.
- To explore NLP-based approaches for mental health assessment.
Main Methods:
- Utilizing affective word lists to analyze text data.
- Comparing text features between groups diagnosed with depression or burnout and a healthy control group.
- Employing statistical analysis to evaluate differences in arousal levels.
Main Results:
- Affective word list ratings successfully differentiated between texts indicating depression/burnout and control texts.
- Texts associated with depression and burnout exhibited statistically significant higher arousal compared to the control group.
- Demonstrated the potential of NLP in identifying subtle linguistic markers of mental health conditions.
Conclusions:
- Affective word list analysis is a viable method for distinguishing depression and burnout from non-clinical text.
- Higher emotional arousal detected through text analysis may serve as an indicator for depression and burnout.
- NLP techniques present a promising, objective tool for augmenting mental health research and potentially clinical screening.
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