Understanding emotions is key to Affective Computing. Emotion recognition focuses on the communicative component of emotions encoded in social signals. This view alone is insufficient for a deeper understanding and computational representation of the internal, subjectively experienced component of emotions. This article presents a cognition-based method called Deep as a starting point for deeper computational modeling of the internal component of emotions. Deep incorporates an approach to query individual internal emotional experiences and to represent such information computationally. It combines social signals, verbalized introspection information, context information, and theory-driven knowledge. We apply the Deep method to the emotion of shame as an example and compare it to a typical emotion recognition model, highlighting the differences and advantages.