Ranking social emotions by learning listwise preference
Abstract-Emotion modeling has received a great attention in recent years. This paper models the online social emotions that are the online users' emotional responds when they are exposed to news articles. Specifically, we rank social emotion labels for online documents. Unlike the existing method, referred to as Pair-LR, which learns pairwise preference and adopts binary classification, we address the problem of ranking social emotions by learning listwise preference. In particular, a novel approach, referred to as List-LR, is proposed to learn a ranking model for social emotion labels of online documents by minimizing the listwise loss defined on instances. Empirical experiments show that the proposed approach outperforms Pair-LR and is also competitive to other two start-of-the-art approaches for label ranking.
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