A cluster-profile representation of emotion using agglomerative hierarchical clustering

The proper representation of emotion is critical to automatic classification systems. In previous research, we demonstrated that emotion profile (EP) based representations are effective for this task. In EP-based representations, emotions are expressed in terms of underlying affective components from the subset of anger, happiness, neutrality, and sadness. The current study explores cluster profiles (CP), an alternate profile representa-tion in which the components are no longer semantic labels, but clusters inherent in the feature space. This unsupervised clus-tering of the feature space permits the application of a system-level semi-supervised learning paradigm. The results demon-strate that CPs are similarly discriminative to EPs (EP classifica-tion accuracy: 68.37 % vs. 69.25 % for the CP-based classifica-tion). This suggests that exhaustive labeling of a representative training corpus may not be necessary for emotion classification tasks.