Understanding the emotions that individuals in crisis express is a clinically relevant goal. Here, we introduce an automated method for extracting present and past personal emotion expressions from text-based crisis conversations, enabling nuanced analyses of how these emotional profiles vary by age. We develop a three-tier emotion taxonomy and leverage both real conversation data and synthetic sentences to train a transformer-based model that captures contextual distinctions between true personal emotion expressions and other mentions. Our RoBERTa-based classifier outperforms both a regex baseline and a model trained only on real conversation data, achieving an F1 score of 0.856. Subsequent analysis of 338,924 crisis conversations shows that age is correlated with distinct patterns in emotional expressions. These findings underscore the clinical value of age-sensitive emotion analysis and constitute an initial step toward characterizing lexical variations across demographic groups.