Hierarchical Approach to Emotion Recognition and Classification in Texts

Diman Ghazi, Diana Inkpen, Stan Szpakowicz

Abstract: We explore the task of automatic classification of texts by the emotions expressed. We consider how the presence of neutral instances affects the performance of distinguishing between emotions. Another facet of the evaluation concerns the relation between polarity and emotions. We apply a novel approach which arranges neutrality, polarity and emotions hierarchically. This method significantly outperforms the corresponding “flat” approach which does not take into account the hierarchical information. We also compare corpus-based and lexical-based feature sets and we choose the most appropriate set of features to be used in our hierarchical classification experiments.