Paper: Ranking Reader Emotions Using Pairwise Loss Minimization and Emotional Distribution Regression

ACL ID D08-1015
Title Ranking Reader Emotions Using Pairwise Loss Minimization and Emotional Distribution Regression
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

This paper presents two approaches to ranking reader emotions of documents. Past studies assign a document to a single emotion cate- gory, so their methods cannot be applied di- rectly to the emotion ranking problem. Furthermore, whereas previous research ana- lyzes emotions from the writer’s perspective, this work examines readers’ emotional states. The first approach proposed in this paper minimizes pairwise ranking errors. In the sec- ond approach, regression is used to model emotional distributions. Experiment results show that the regression method is more ef- fective at identifying the most popular emo- tion, but the pairwise loss minimization method produces ranked lists of emotions that have better correlations with the correct lists.