Information, Prediction, and Query by Committee
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Multi-criteria-based active learning for named entity recognition
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Actively Transfer Domain Knowledge
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Document-Word Co-regularization for Semi-supervised Sentiment Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
IEEE Transactions on Knowledge and Data Engineering
Employing personal/impersonal views in supervised and semi-supervised sentiment classification
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Domain adaptation meets active learning
ALNLP '10 Proceedings of the NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing
Automatically extracting polarity-bearing topics for cross-domain sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Relationships between Diversity of Classification Ensembles and Single-Class Performance Measures
IEEE Transactions on Knowledge and Data Engineering
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In the literature, various approaches have been proposed to address the domain adaptation problem in sentiment classification (also called cross-domain sentiment classification). However, the adaptation performance normally much suffers when the data distributions in the source and target domains differ significantly. In this paper, we suggest to perform active learning for cross-domain sentiment classification by actively selecting a small amount of labeled data in the target domain. Accordingly, we propose an novel active learning approach for cross-domain sentiment classification. First, we train two individual classifiers, i.e., the source and target classifiers with the labeled data from the source and target respectively. Then, the two classifiers are employed to select informative samples with the selection strategy of Query By Committee (QBC). Third, the two classifier is combined to make the classification decision. Importantly, the two classifiers are trained by fully exploiting the unlabeled data in the target domain with the label propagation (LP) algorithm. Empirical studies demonstrate the effectiveness of our active learning approach for cross-domain sentiment classification over some strong baselines.