Emotion detection AI is emerging as a promising alternative to self-report measures in intensive longitudinal data designs, such as ecological momentary assessment (EMA). For example, AI is able to retrieve emotion estimates at much faster intervals than traditional self-report measures and without the cognitive interference from reading and answering, which are present in all psychometric instruments. However, most studies involving AI are only able to interpret large numbers of emotion estimates idiographically, and there lacks a clear framework for how to interpret them nomothetically. Thus, we propose a novel framework for transforming emotion detection AI estimates in order to maximize their utility in EMA research. Our approach uses group iterative multiple model estimation (GIMME) to identify lagged emotion subgroups and estimate the closeness of fit for each individual network. Implications in both confirmatory and exploratory studies are discussed.