Abstract:
“The question of representing emotion computationally remains largely unanswered: popular approaches require annotators to assign a magnitude (or a class) of some emotional dimension, while an alternative is to focus on the relationship between two or more options. Recent evidence in affective computing suggests that following a methodology of ordinal annotations and processing leads to better reliability and validity of the model. This paper compares the generality of classification methods versus preference learning methods in predicting the levels of arousal in two widely used affective datasets. Findings of this initial study further validate the hypothesis that approaching affect labels as ordinal data and building models via preference learning yields models of better validity.”
Citation:
David Melhart, Konstantinos Sfikas, Giorgos Giannakakis, Georgios N. Yannakakis and Antonios Liapis: “A Study on Affect Model Validity: Nominal vs Ordinal Labels,” in Proceedings of the IJCAI workshop on AI and Affective Computing, 2018.
BibTeX:
inproceedings{melhart2018study,
author={David Melhart and Konstantinos Sfikas and Giorgos Giannakakis and Georgios N. Yannakakis and Antonios Liapis},
title={A Study on Affect Model Validity: Nominal vs Ordinal Labels},
booktitle={Proceedings of the IJCAI workshop on AI and Affective Computing},
year={2018},
}
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