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Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification
May-June 2013 (vol. 28 no. 3)
pp. 10-18
Rui Xia, Nanjing University of Science and Technology
Chengqing Zong, Chinese Academy of Sciences
Xuelei Hu, Nanjing University of Science and Technology
Erik Cambria, National University of Singapore
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.
Index Terms:
Classification,Natural language processing,Adaptation models,Principal component analysis,Intelligent systems,Computational linguistics,Text analysis,sample selection,Classification,Natural language processing,Adaptation models,Principal component analysis,Intelligent systems,Computational linguistics,Text analysis,intelligent systems,sentiment classification,domain adaptation,instance adaptation,labeling adaptation
Citation:
Rui Xia, Chengqing Zong, Xuelei Hu, Erik Cambria, "Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification," IEEE Intelligent Systems, vol. 28, no. 3, pp. 10-18, May-June 2013, doi:10.1109/MIS.2013.27
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