Two-View Label Propagation to Semi-supervised Reader Emotion Classification

Shoushan Li, Jian Xu, Dong Zhang, Guodong Zhou

Abstract
In the literature, various supervised learning approaches have been adopted to address the task of reader emotion classification. However, the classification performance greatly suffers when the size of the labeled data is limited. In this paper, we propose a two-view label propagation approach to semi-supervised reader emotion classification by exploiting two views, namely source text and response text in a label propagation algorithm. Specifically, our approach depends on two word-document bipartite graphs to model the relationship among the samples in the two views respectively. Besides, the two bipartite graphs are integrated by linking each source text sample with its corresponding response text sample via a length-sensitive transition probability. In this way, our two-view label propagation approach to semi-supervised reader emotion classification largely alleviates the reliance on the strong sufficiency and independence assumptions of the two views, as required in co-training. Empirical evaluation demonstrates the effectiveness of our two-view label propagation approach to semi-supervised reader emotion classification.
Anthology ID:
C16-1249
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2647–2655
Language:
URL:
https://aclanthology.org/C16-1249
DOI:
Bibkey:
Cite (ACL):
Shoushan Li, Jian Xu, Dong Zhang, and Guodong Zhou. 2016. Two-View Label Propagation to Semi-supervised Reader Emotion Classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2647–2655, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Two-View Label Propagation to Semi-supervised Reader Emotion Classification (Li et al., COLING 2016)
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https://preview.aclanthology.org/nschneid-patch-4/C16-1249.pdf
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