AI-based approach to burnout identification from textual data
This addresses burnout identification for workers in high-stress environments, but it is incremental as it adapts an existing model to a new task.
The study tackled burnout detection from textual data by fine-tuning a RuBERT model on synthetic and YouTube comment data, achieving a model that assigns burnout probabilities to texts for monitoring in high-stress environments.
This study introduces an AI-based methodology that utilizes natural language processing (NLP) to detect burnout from textual data. The approach relies on a RuBERT model originally trained for sentiment analysis and subsequently fine-tuned for burnout detection using two data sources: synthetic sentences generated with ChatGPT and user comments collected from Russian YouTube videos about burnout. The resulting model assigns a burnout probability to input texts and can be applied to process large volumes of written communication for monitoring burnout-related language signals in high-stress work environments.