Feature ensemble plus sample selection: domain adaptation for sentiment classification

Xia, Rui, Zong, Chengqing, Hu, Xuelei and Cambria, Erik (2013) Feature ensemble plus sample selection: domain adaptation for sentiment classification. IEEE Intelligent Systems, 28 3: 10-18. doi:10.1109/MIS.2013.27


Author Xia, Rui
Zong, Chengqing
Hu, Xuelei
Cambria, Erik
Title Feature ensemble plus sample selection: domain adaptation for sentiment classification
Journal name IEEE Intelligent Systems   Check publisher's open access policy
ISSN 1541-1672
1941-1294
Publication date 2013-05-01
Sub-type Article (original research)
DOI 10.1109/MIS.2013.27
Open Access Status Not yet assessed
Volume 28
Issue 3
Start page 10
End page 18
Total pages 9
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Abstract Domain adaptation problems often arise often in the field of sentiment classification. Here, the feature ensemble plus sample selection (SS-FE) approach is proposed, which takes labeling and instance adaptation into account. A feature ensemble (FE) model is first proposed to learn a new labeling function in a feature reweighting manner. Furthermore, a PCA-based sample selection (PCA-SS) method is proposed as an aid to FE. Experimental results show that the proposed SS-FE approach could gain significant improvements, compared to FE or PCA-SS, because of its comprehensive consideration of both labeling adaptation and instance adaptation.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status Non-UQ
Additional Notes Published May-June 2013.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
School of Information Technology and Electrical Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 60 times in Thomson Reuters Web of Science Article | Citations
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Created: Mon, 16 Dec 2013, 22:02:19 EST by Xuelei Hu on behalf of School of Information Technol and Elec Engineering