Please use this identifier to cite or link to this item:
Title: Cluster-level emotion pattern matching for cross-domain social emotion classification
Author(s): Xie, Haoran 
Wang, Philips Fu Lee 
Author(s): Zhu, E.
Rao, Y.
Liu, Y.
Yin, J.
Issue Date: 2017
Publisher: Association for Computing Machinery
Related Publication(s): CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
Start page: 2435
End page: 2438
This paper addresses the task of cross-domain social emotion classification of online documents. The cross-domain task is formulated as using abundant labeled documents from a source domain and a small amount of labeled documents from a target domain, to predict the emotion of unlabeled documents in the target domain. Although several cross-domain emotion classification algorithms have been proposed, they require that feature distributions of different domains share a sufficient overlapping, which is hard to meet in practical applications. This paper proposes a novel framework, which uses the emotion distribution of training documents at the cluster level, to alleviate the aforementioned issue. Experimental results on two datasets show the effectiveness of our proposed model on cross-domain social emotion classification.
DOI: 10.1145/3132847.3133063
CIHE Affiliated Publication: Yes
Appears in Collections:CIS Publication

Files in This Item:
File Description SizeFormat
View Online126 BHTMLView/Open
SFX Query Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.