A perspective view and survey of meta-learning
Artificial Intelligence Review
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
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
Mining association language patterns for negative life event classification
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
A comparative study of Bayesian models for unsupervised sentiment detection
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Negative training data can be harmful to text classification
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Sentiment classification and polarity shifting
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Active deep networks for semi-supervised sentiment classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Multi-domain sentiment classification with classifier combination
Journal of Computer Science and Technology - Special issue on natural language processing
Collective classification of congressional floor-debate transcripts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A new domain adaptation method based on rules discovered from cross-domain features
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Cross-domain co-extraction of sentiment and topic lexicons
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Active learning for imbalanced sentiment classification
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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
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This paper addresses a new task in sentiment classification, called multi-domain sentiment classification, that aims to improve performance through fusing training data from multiple domains. To achieve this, we propose two approaches of fusion, feature-level and classifier-level, to use training data from multiple domains simultaneously. Experimental studies show that multi-domain sentiment classification using the classifier-level approach performs much better than single domain classification (using the training data individually).