Proceedings of the Annual Meeting of the Cognitive Science Society

While research on emotions has become one of the most pro-ductive areas at the intersection of cognitive science, artifi-cial intelligence and natural language processing, the diversityand incommensurability of emotion models seriously hampersprogress in the field. We here propose kNN regression as asimple, yet effective method for computationally mapping be-tween two major strands of emotion representations, namelydimensional and discrete emotion models. In a series of ma-chine learning experiments on data sets of textual stimuli wegather evidence that this approach reaches a human level ofreliability using a relatively small number of data points only.