Abstract
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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.