Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Using appraisal groups for sentiment analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
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
Chinese lexical analysis using hierarchical hidden Markov model
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
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
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Lexicon based sentiment analysis of Urdu text using SentiUnits
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
MIEA: a mutual iterative enhancement approach for cross-domain sentiment classification
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Which clustering do you want? inducing your ideal clustering with minimal feedback
Journal of Artificial Intelligence Research
Weighted SCL model for adaptation of sentiment classification
Expert Systems with Applications: An International Journal
Transfer learning via multi-view principal component analysis
Journal of Computer Science and Technology - Special issue on natural language processing
A random walk algorithm for automatic construction of domain-oriented sentiment lexicon
Expert Systems with Applications: An International Journal
Filling the gap: semi-supervised learning for opinion detection across domains
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Language-independent sentiment classification using three common words
Proceedings of the 20th ACM international conference on Information and knowledge management
Using key sentence to improve sentiment classification
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
A generate-and-test method of detecting negative-sentiment sentences
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Cross-media sentiment classification and application to box-office forecasting
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Bootstrapping polarity classifiers with rule-based classification
Language Resources and Evaluation
Associating targets with SentiUnits: a step forward in sentiment analysis of Urdu text
Artificial Intelligence Review
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In the community of sentiment analysis, supervised learning techniques have been shown to perform very well. When transferred to another domain, however, a supervised sentiment classifier often performs extremely bad. This is so-called domain-transfer problem. In this work, we attempt to attack this problem by making the maximum use of both the old-domain data and the unlabeled new-domain data. To leverage knowledge from the old-domain data, we proposed an effective measure, i.e., Frequently Co-occurring Entropy (FCE), to pick out generalizable features that occur frequently in both domains and have similar occurring probability. To gain knowledge from the new-domain data, we proposed Adapted Naïve Bayes (ANB), a weighted transfer version of Naive Bayes Classifier. The experimental results indicate that proposed approach could improve the performance of base classifier dramatically, and even provide much better performance than the transfer-learning baseline, i.e. the Naïve Bayes Transfer Classifier (NTBC).