A critical task in Affective Computing is the reliable assessment of emotional states. The two most prominent approaches to classify emotions are categorical concepts of discrete emotions (e.g. OCC) and dimensional models typically using the pleasure - arousal - dominance space (PAD). In current research and applications, however, there is little overlap between these two concepts. A mapping of discrete categories into the dimensional space would offer new possibilities to model the emotional states of users and artificial agents, though. We hence let N=70 healthy subjects place the labels of discrete OCC emotions into PAD space according to their subjective knowledge with a simple visual tool. There was a high inter-subject consistency regarding the positioning of OCC emotions for the dimension of pleasure. However, arousal and dominance ratings showed considerably greater variance. We conclude that global and reliable mappings of OCC emotions into the PAD space can best be provided for the pleasure dimension. The exact positioning of discrete emotions regarding arousal and dominance can only be gained by individual calibration of a given user in a strict within-subject approach.