Unveiling Mental Health Insights: A Novel NLP Tool for Stress Detection through Writing and Speaking Analysis to Prevent Burnout

Abstract: Nowadays, innovative approaches that precisely identify and treat health-related problems are becoming more and more necessary in a time of rapid technological advancement and growing mental health awareness. Given the prevalence of mental health issues, different tools that employ Artificial Intelligence to support rapid and effective interventions have been developed. This study focuses on the relationship between language expression and mental health, recognizing subtle nuances in both written and spoken communication as potential stress indicators and presenting a novel AI enhanced tool for autonomous and passive stress detection.Specifically, in our study data scientists and psychologists collaborate to create and validate a groundbreaking knowledge base. This innovative database combines psychometrics, biometrics, and linguistic analysis to provide a comprehensive evaluation of stress levels. We used biomedical indicators, such as blood pressure, heart rate variability (HRV), and cortisol levels correlations to validate the results. The multidisciplinary team brought together expertise from data science and psychology to create a novel database with a wide range of sentences that have been annotated with matching stress levels.Thanks to this strong psychometric framework for correlating language manifestation of stress with clinical diagnosis, we developed the first, to our knowledge, NLP (Natural Language Processing) tool for autonomous and passive stress detection. This includes a variety of emotional and cognitive stress indicators to provide a deeper understanding of stress that takes into account both subjective experiences and objective manifestations. Initial results show a strong relationship between the biomedical markers and the stress scores obtained from language analysis. By combining data science techniques with psychometric insights, our stress detection achieves 83% in terms of F1 score, providing a more complete picture of a person's stress profile.During the entire study, ethical considerations were taken into account, following well defined data privacy and protection protocols. In fact, before any data was added to the database, participants were carefully informed about the purpose of data collection.Workplace communication platforms may be combined with our NLP technology to track employee well-being in a professional context. This includes real-time alerts to managers and HR specialists, allowing for timely interventions and promoting a collaborative and positive work environment. The strong correlation between clinical metrics and linguistic semantic choices represents a significant step toward the reform of mental health care. In addition the impressive accuracy of the tool we developed provides a reliable support system for spotting stress symptoms in both written and spoken communication. This should help us to change the way we think about stress, assisting us to assess the presence of a burnout condition before it escalates into more serious health issues. The implementation of this technology into various elements of daily life has the potential to revolutionize society perceptions on mental health, allowing for a more in-depth knowledge of the multiple components involved with stress.

Keywords: Artificial intelligence, Natural language processing, Mental health

DOI: 10.54941/ahfe1004653

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