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Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification

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4 Author(s)
Rui Xia ; Nanjing Univ. of Sci. & Technol., Nanjing, China ; Chengqing Zong ; Xuelei Hu ; Cambria, E.

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.

Published in:

Intelligent Systems, IEEE  (Volume:28 ,  Issue: 3 )