The term emotion is, despite its frequent use, still mysterious to researchers. This poses difficulties on the task of automatic emotion detection in text. At the same time, applications for emotion classifiers increase steadily in today's digital society where humans are constantly interacting with machines. Hence, the need for improvement of current state-of-the-art emotion classifiers arises. The Swiss psychologist Klaus Scherer published an emotion model according to which an emotion is composed of changes in the five components cognitive appraisal, physiological symptoms, action tendencies, motor expressions, and subjective feelings. This model, which he calls CPM gained reputation in psychology and philosophy, but has so far not been used for NLP tasks. With this work, we investigate, whether it is possible to automatically detect the CPM components in social media posts and, whether information on those components can aid the detection of emotions. We create a text corpus consisting of 2100 Twitter posts, that has every instance labeled with exactly one emotion and a binary label for each CPM component. With a Maximum Entropy classifier we manage to detect CPM components with an average F1-score of 0.56 and average accuracy of 0.82 on this corpus. Furthermore, we compare baseline versions of one Maximum Entropy and one CNN emotion classifier to extensions of those classifiers with the CPM annotations and predictions as additional features. We find slight performance increases of up to 0.03 for the F1-score for emotion detection upon incorporation of CPM information.