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Burnout, a syndrome conceptualized as resulting from major workplace stress that has not been successfully managed, is a major problem of today's society, in particular in crisis times such as a global pandemic situation. Burnout detection is hard, because the symptoms often overlap with other diseases and syndromes. Typical clinical approaches are using inventories to assess burnout for their patients, even though free-text approaches are considered promising. In research of natural language processing (NLP) applied to mental health, often data from social media is used and not real patient data, which leads to some limitations for the application in clinical use cases. In this paper, we fill the gap and provide a dataset using extracts from interviews with burnout patients containing 216 records. We train a support vector machine (SVM) classifier to detect burnout in text snippets with an accuracy of around 80%, which is clearly higher than the random baseline of our setup. This provides the foundation for a next generation of clinical methods based on NLP. |
