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Open Access

An Ensemble Approach for Emotion Cause Detection with Event Extraction and Multi-Kernel SVMs

Ruifeng Xu, Jiannan Hu, Qin Lu, Dongyin Wu, Lin Gui( )
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen 518055, China.
Department of Computing, the Hong Kong Polytechnic University, Hong Kong, China.
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Abstract

In this paper, we present a new challenging task for emotion analysis, namely emotion cause extraction. In this task, we focus on the detection of emotion cause a.k.a the reason or the stimulant of an emotion, rather than the regular emotion classification or emotion component extraction. Since there is no open dataset for this task available, we first designed and annotated an emotion cause dataset which follows the scheme of W3C Emotion Markup Language. We then present an emotion cause detection method by using event extraction framework, where a tree structure-based representation method is used to represent the events. Since the distribution of events is imbalanced in the training data, we propose an under-sampling-based bagging algorithm to solve this problem. Even with a limited training set, the proposed approach may still extract sufficient features for analysis by a bagging of multi-kernel based SVMs method. Evaluations show that our approach achieves an F-measure 7.04% higher than the state-of-the-art methods.

Keywords

emotion cause detectionevent extractionmulti-kernel SVMsbagging

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Tsinghua Science and Technology
Volume 22 Issue 6,
December 2017
Pages 646-659
DOI: 10.23919/TST.2017.8195347
Cite this article:
Xu R, Hu J, Lu Q, et al. An Ensemble Approach for Emotion Cause Detection with Event Extraction and Multi-Kernel SVMs. Tsinghua Science and Technology, 2017, 22(6): 646-659. https://doi.org/10.23919/TST.2017.8195347

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Received: 02 January 2017
Accepted: 26 March 2017
Published: 14 December 2017
© The author(s) 2017
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