ROAR: Robust Label Ranking for Social Emotion Mining

Authors

  • Jason (Jiasheng) Zhang The Pennsylvania State University
  • Dongwon Lee The Pennsylvania State University

Keywords:

Label Ranking, Imbalanced Data

Abstract

Understanding and predicting latent emotions of users toward online contents, known as social emotion mining, has become increasingly important to both social platforms and businesses alike. Despite recent developments, however, very little attention has been made to the issues of nuance, subjectivity, and bias of social emotions. In this paper, we fill this gap by formulating social emotion mining as a robust label ranking problem, and propose: (1) a robust measure, named as G-mean-rank (GMR), which sets a formal criterion consistent with practical intuition; and (2) a simple yet effective label ranking model, named as ROAR, that is more robust toward unbalanced datasets (which are common). Through comprehensive empirical validation using 4 real datasets and 16 benchmark semi-synthetic label ranking datasets, and a case study, we demonstrate the superiorities of our proposals over 2 popular label ranking measures and 6 competing label ranking algorithms. The datasets and implementations used in the empirical validation are available for access.

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Published

2018-04-29

How to Cite

Zhang, J. (Jiasheng), & Lee, D. (2018). ROAR: Robust Label Ranking for Social Emotion Mining. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11724