Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification

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.

Authors

• Rui Xia
• Chengqing Zong State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
• Xuelei Hu
• Erik Cambria Nanyang Technological University, Singapore

Keywords

• Classification
• Natural language processing
• Adaptation models
• Principal component analysis
• Intelligent systems
• Computational linguistics
• Text analysis
• Sentiment Analysis
• Domain Adaptation
• Ensemble Feature
• Training Data
• Validation Set
• Singular Value
• Subset Of Samples
• Singular Value Decomposition
• Base Classifiers
• Domain Data
• Target Domain
• Postage
• Conceptual Space
• Adverbs
• Source Domain
• Paired Box
• Baseline System
• Kullback-Leibler Distance
• Sentiment Index
• Types Of Tags
• Latent Concept
• sentiment classification
• instance adaptation
• labeling adaptation
• sample selection