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Title: Joint modeling of news reader's and comment writer's emotions
Authors: Liu, H
Li, S
Zhou, G
Huang, CR 
Li, P
Issue Date: 2013
Publisher: Association for Computational Linguistics (ACL)
Source: ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 2013, v. 2, p. 511-515 How to cite?
Abstract: Emotion classification can be generally done from both the writer's and reader's perspectives. In this study, we find that two foundational tasks in emotion classification, i.e., reader's emotion classification on the news and writer's emotion classification on the comments, are strongly related to each other in terms of coarse-grained emotion categories, i.e., negative and positive. On the basis, we propose a respective way to jointly model these two tasks. In particular, a co-training algorithm is proposed to improve semi-supervised learning of the two tasks. Experimental evaluation shows the effectiveness of our joint modeling approach.
Description: 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013, Sofia, 4-9 August 2013
ISBN: 9781937284510
Appears in Collections:Conference Paper

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