Assessment of Work-Related Subjective Well-Being Using Natural Language Processing of Employee Interviews. A Proof of Principle Investigation

The present study investigated whether work-related subjective well-being (SWB) can be assessed using employee responses to interview questions. Our objective was to provide proof-of-principle evidence that unstructured language can be used to simultaneously predict multiple SWB components. To achieve this goal, we asked 386 employees (52% women) from various industries to complete self-reported measures of SWB, and then we conducted individual interviews. The responses collected during structured interviews were analyzed using transformer-based models to extract semantic characteristics. Next, the semantic characteristics were used to predict multiple SWB indicators. Results showed that descriptions of typical work activities offered fair predictive accuracy of SWB scales, performing better than narratives focused on positive or on negative experiences. Furthermore, simpler machine learning algorithms such as Naïve Bayes achieved higher accuracy than more complex models, demonstrating the effectiveness of transformers-based approaches. Although the study has limitations, the results provide a foundation for using NLP in assessments of SWB, opening the way for tools that are customizable and text-sensitive.