Ranking social emotions by learning listwise preference

Authors

Qishen Wang
Ou Wu
Weiming Hu, Institute of Automation, Beijing, China
Jinfeng Yang
Wanqing Li, University of WollongongFollow

RIS ID

54651

Publication Details

Wang, Q., Wu, O., Hu, W., Yang, J. & Li, W. (2011). Ranking social emotions by learning listwise preference. First Asian Conference on Digital Object Identifier (pp. 164-168). USA: IEEE.

Abstract

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.