Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24469
Title: Joint learning on sentiment and emotion classification
Authors: Gao, W
Li, S
Lee, SYM 
Zhou, G
Huang, CR 
Keywords: Emotion classification
Joint learning
Sentiment classification
Issue Date: 2013
Source: International Conference on Information and Knowledge Management, Proceedings, 2013, p. 1505-1508 How to cite?
Abstract: Sentiment and emotion classification have been popularly but separately studied in natural language processing. In this paper, we address joint learning on sentiment and emotion classification where both the labeled data for sentiment and emotion classification are available. The objective of this joint-learning is to benefit the two tasks from each other for improving their performances. Specifically, an extra data set that is annotated with both sentiment and emotion labels are employed to estimate the transformation probability between the two kinds of labels. Furthermore, the transformation probability is leveraged to transfer the classification labels to benefit the two tasks from each other. Empirical studies demonstrate the effectiveness of our approach for the novel joint learning task.
Description: 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013, San Francisco, CA, 27 October-1 November 2013
URI: http://hdl.handle.net/10397/24469
ISBN: 9781450322638
DOI: 10.1145/2505515.2507830
Appears in Collections:Conference Paper

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