When Psychological Change Becomes Textually Latent: A Viewpoint on Retrospective Longitudinal Analysis of Reflective Mental Health Text (Preprint)

BACKGROUND Digital mental health applications increasingly deploy sentiment analysis tools to monitor psychological change. While these tools effectively detect affective transformations (emotional valence changes), their performance on cognitive-structural transformations (identity integration, meaning-making, acceptance development) remains underexplored. OBJECTIVE This Viewpoint evaluates whether standard affect-detection tools can capture cognitive-structural transformations, using a longitudinal single-subject corpus exhibiting both transformation types. METHODS Using a single-subject, multi-environment corpus spanning both introspective therapeutic dialogue and emotionally expressive casual chat, we compared three widely used paradigms: deterministic lexicon accumulation, supervised emotion classification, and LLM-based message-level affect inference. Lexicon-based (Phase A) and supervised models (Phase B) marginally tracked affective change across time in both expressive and reflective contexts. LLM-based inference (Phase C), however, failed to detect drift in introspective text while succeeding in high-affect, stylistically foregrounded language, the casual communications corpus. RESULTS While these methods detected modest affective changes in casual conversation, they showed no systematic differentiation within the therapeutic corpus once message length and aggregation effects were considered. In contrast, construct-specific analyses focused on identity integration and acceptance language, together with longitudinal contextual interpretation, identified a coherent psychological transformation corroborated by self-report and behavioral change. Without message length normalization, therapeutic progress could be confounded with changes in verbosity, elaboration, or reflective depth -all of which may increase during productive therapeutic engagement independent of emotional state changes. CONCLUSIONS Standard sentiment tools remain effective for detecting affective change but as illustrated by this case, might be insufficient for identifying meaning-oriented psychological transformation. Future digital mental health research should distinguish between affective and cognitive change phenotypes and develop construct-appropriate analytic frameworks to avoid systematic blind spots