Evaluation of Unsupervised Emotion Models to Textual Affect Recognition
Sunghwan Mac Kim, Alessandro Valitutti, Rafael A. Calvo · 2010
In this paper we present an evaluation of new techniques for automatically detecting emotions in text. The study estimates categorical model and dimensional model for the recognition of four affective states: Anger, Fear, Joy, and Sadness that are common emotions in three datasets: SemEval-2007 “Affective Text”, ISEAR (International Survey on Emotion Antecedents and Reactions), and children’s fairy tales. In the first model, WordNet-Affect is used as a linguistic lexical resource and three dimensionality reduction techniques