Well-being research and advanced natural language processing: prospects and limitations

<p style="text-align: justify;"><strong>Context and relevance.</strong> Well-being research faces methodological limitations of conventional psychometric measures, criticized for poor ecological validity, limited information yield, and inadequate capture of multidimensional construct of well-being. Advanced natural language processing (NLP) technologies offer solutions to these constraints. <strong>Objective.</strong> To evaluate opportunities and challenges of transformer-based NLP for well-being research. <strong>Methods and materials.</strong> We conducted an analytical review of current literature examining NLP applications in well-being studies. Our analysis includes a technical overview of transformer-based NLP systems, focusing on: (1) textual data versus traditional scales, and (2) methodological implications. <strong>Results.</strong> The reviewed NLP approaches demonstrate three principal advantages: (1) enhanced granularity and information density in linguistic data, (2) superior ecological validity relative to standardized scales, and (3) improved resource efficiency. However, we identify significant limitations, particularly the inadequate methodological conceptualization of these tools, which fails to keep pace with their rapid technological evolution. <strong>Conclusions.</strong> While NLP methodologies show considerable promise for advancing well-being research, their effective implementation requires substantial methodological groundwork. Essential prerequisites include: (1) establishing robust validity and reliability assessment protocols, and (2) developing comprehensive epistemological frameworks for their application.</p> <div class="XTranslate" style="all: unset;"></div>