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.
CITATION STYLE
Alcaide, J. M., Justo, R., & Torres, M. I. (2015). Combining statistical and semantic knowledge for sarcasm detection in online dialogues. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9117, pp. 662–671). Springer Verlag. https://doi.org/10.1007/978-3-319-19390-8_74
Mendeley helps you to discover research relevant for your work.