Hierarchical versus Flat Classification of Emotions in Text
Diman Ghazi, Diana Zaiu Inkpen, Stan Śzpakowicz · North American Chapter of the Association for Computational Linguistics · 2010
We explore the task of automatic classification of texts by the emotions expressed. Our novel method arranges neutrality, polarity and emotions hierarchically. We test the method on two datasets and show that it outperforms the corresponding flat approach, which does not take into account the hierarchical information. The highly imbalanced structure of most of the datasets in this area, particularly the two datasets with which we worked, has a dramatic effect on the performance of classification. The hierarchical approach helps alleviate the effect.