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Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification (Extended Abstract)

Rui Xia, Chengqing Zong, Xuelei Hu, Erik Cambria

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Journal Track. Pages 4229-4232.

Abstract

The domain adaptation problem arises often in the field of sentiment classification. There are two distinct needs in domain adaptation, namely labeling adaptation and instance adaptation. Most of current research focuses on the former one, while neglects the latter one. In this work, we propose a joint approach, named feature ensemble plus sample selection (SS-FE), which takes both types of adaptation into account. A feature ensemble (FE) model is first proposed to learn a new labeling function in a feature re-weighting manner. Furthermore, a PCA-based sample selection (PCA-SS) method is proposed as an aid to FE for instance adaptation. Experimental results show that the proposed SS-FE approach could gain significant improvements, compared to individual FE and PCA-SS, due to its comprehensive consideration of both labeling adaptation and instance adaptation.

Rui Xia, Chengqing Zong, Xuelei Hu, and Erik Cambria. 2015. Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification (Extended Abstract). In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 4229–4232. https://ijcai.org/proceedings/2015/601
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