A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
A perspective view and survey of meta-learning
Artificial Intelligence Review
Proceedings of the First International Workshop on Multiple Classifier Systems
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Analysis of Linear and Order Statistics Combiners for Fusion of Imbalanced Classifiers
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
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 sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
Multi-domain sentiment classification
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Comparative experiments on sentiment classification for online product reviews
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Automatic seed word selection for unsupervised sentiment classification of Chinese text
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Feature subsumption for opinion analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Online methods for multi-domain learning and adaptation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Classifier combining rules under independence assumptions
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Dual word and document seed selection for semi-supervised sentiment classification
Proceedings of the 21st ACM international conference on Information and knowledge management
Active learning on sentiment classification by selecting both words and documents
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
Employing emotion keywords to improve cross-domain sentiment classification
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
Constructing chinese sentiment lexicon using bilingual information
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
Measuring feature distributions in sentiment classification
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
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State-of-the-arts studies on sentiment classification are typically domain-dependent and domain-restricted. In this paper, we aim to reduce domain dependency and improve overall performance simultaneously by proposing an efficient multi-domain sentiment classification algorithm. Our method employs the approach of multiple classifier combination. In this approach, we first train single domain classifiers separately with domain specific data, and then combine the classifiers for the final decision. Our experiments show that this approach performs much better than both single domain classification approach (using the training data individually) and mixed domain classification approach (simply combining all the training data). In particular, classifier combination with weighted sum rule obtains an average error reduction of 27.6% over single domain classification.