Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
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
A two-stage approach to domain adaptation for statistical classifiers
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A novel scheme for domain-transfer problem in the context of sentiment analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Using unlabeled data to handle domain-transfer problem of semantic detection
Proceedings of the 2008 ACM symposium on Applied computing
A survey on sentiment detection of reviews
Expert Systems with Applications: An International Journal
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
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In recent years, Structural Correspondence Learning (SCL) is becoming one of the most promising techniques for sentiment-transfer learning. However, SCL model treats each feature as well as each instance by an equivalent-weight strategy. To address the two issues effectively, we proposed a weighted SCL model (W-SCL), which weights the features as well as the instances. More specifically, W-SCL assigns a smaller weight to high-frequency domain-specific (HFDS) features and assigns a larger weight to instances with the same label as the involved pivot feature. The experimental results indicate that proposed W-SCL model could overcome the adverse influence of HFDS features, and leverage knowledge from labels of instances and pivot features.