Joint estimation of multiple affects from crowdsourced annotations

抄録

Expression of emotion, namely affect, is an important part of natural language. For a text-based affect prediction system using supervised learning algorithms, quality of training data is critical to its performance. This paper explores some methods for estimating multiple affects from multi-affect annotations obtained by crowdsourcing, taking into consideration relationships among the affects.