Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/35673
Title: An iterative emotion classification approach for microblogs
Authors: Xu, RF
Wang, ZY
Xu, J
Chen, JW
Lu, Q 
Wong, KF
Keywords: Emotion Classification
Iterative Classification
Microblogs
Issue Date: 2015
Publisher: Springer
Source: In A. Gelbukh (Ed.), Computational linguistics and intelligent text processing : 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings. Part II, p. 104-113. Cham : Springer, 2015 How to cite?
Series/Report no.: Lecture notes in computer science ; v. 9042
Abstract: The typical emotion classification approach adopts one-step single-label classification using intra-sentence features such as unigrams, bigrams and emotion words. However, single-label classifier with intra-sentence features cannot ensure good performance for short microblogs text which has flexible expressions. Target to this problem, this paper proposes an iterative multi-label emotion classification approach for microblogs by incorporating intra-sentence features, as well as sentence and document contextual information. Based on the prediction of the base classifier with intra-sentence features, the iterative approach updates the prediction by further incorporating both sentence and document contextual information until the classification results converge. Experimental results obtained by three different multi-label classifiers on NLP & CC2013 Chinese microblog emotion classification bakeoff dataset demonstrates the effectiveness of our iterative emotion classification approach.
URI: http://hdl.handle.net/10397/35673
ISBN: 978-3-319-18117-2
978-3-319-18116-5
ISSN: 0302-9743
DOI: 10.1007/978-3-319-18117-2_8
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