Combining Statistical and Semantic Knowledge for Sarcasm Detection in Online Dialogues

The detection of secondary emotions, like sarcasm, in online dialogues is a difficult task that has rarely been treated in the literature. In this work (This work has been partially supported by the Spanish Ministry of Science under grant TIN2011-28169-C05-04, and by the Basque Government under grant IT685-13.), we tackle this problem as an affective pattern recognition problem. Specifically, we consider different kind of information sources (statistical and semantic) and propose alternative ways of combining them. We also provide a comparison of a Support Vector Machine (SVM) classification method with a simpler Naive Bayes parametric classifier. The experimental results show that combining statistical and semantic feature sets comparable performances can be achieved with Naive Bayes and SVM classifiers.