Blind Spots in AI-Based Longitudinal Psychological Inference: A Single-Subject Validation Study

Generic sentiment analysis tools are widely deployed in mental health applications research and longitudinal text analysis to infer psychological change. Lexicon-based tools (NRC Emotion Lexicon), supervised classifiers (GoEmotions), and large language models (GPT-4) are applied across therapeutic text, social media monitoring, organizational communication, and narrative analysis contexts. However, their construct validity for detecting different types of psychological transformation remains underexamined. This study presents a single-subject longitudinal corpus (n=531 reflective messages across six months) to illustrate a construct–measurement mismatch. The subject self-reported substantial psychological change, corroborated by behavioral indicators and triangulated AI-based thematic coding (inter-rater reliability κ=0.72–0.89). Yet standard sentiment tools showed no systematic signal: lexicon counts increased proportionally to message length expansion, supervised classifiers detected minimal directional change (Cohen's d=0.15–0.26), and LLM-based scoring produced minimal or inconsistent trajectories across temporal bins. We argue that this null result is not a failure of AI per se, but a measurement blind spot: when transformation occurs at the level of narrative meaning rather than valence frequency, generic sentiment tools may remain insensitive. We propose a construct-specific analytic framework emphasizing alignment between psychological target constructs and computational operationalization (59% improvement in identity score, 124% increase in acceptance markers, d=0.82). Implications are discussed for responsible deployment of AI in longitudinal digital health and narrative analysis contexts. These findings have direct implications for NLP tool developers and practitioners deploying sentiment analysis in mental health and longitudinal monitoring contexts. Standard sentiment tools exhibit construct-specific validity limitations, systematically missing cognitive–structural transformations while reliably detecting affective transformations. Researchers and developers should align computational measures with theoretical constructs rather than defaulting to generic sentiment analysis.