Evaluation of Unsupervised Emotion Models to Textual Affect Recognition

  title={Evaluation of Unsupervised Emotion Models to Textual Affect Recognition},
  author={Sunghwan Mac Kim and Alessandro Valitutti and Rafael Alejandro Calvo},
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, WordNetAffect is used as a linguistic lexical resource and three… CONTINUE READING
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