Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Document clustering with cluster refinement and model selection capabilities
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Mining product reputations on the Web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Automatic detection of text genre
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Learning to learn with the informative vector machine
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Detection of agreement vs. disagreement in meetings: training with unlabeled data
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
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
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
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
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
Introduction to Information Retrieval
Introduction to Information Retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Management Science
Deep transfer via second-order Markov logic
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Mapping and revising Markov logic networks for transfer learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Multiple Instance Transfer Learning
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Bundle Methods for Regularized Risk Minimization
The Journal of Machine Learning Research
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
IEEE Transactions on Knowledge and Data Engineering
Smokey: automatic recognition of hostile messages
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
ALPOS: A Machine Learning Approach for Analyzing Microblogging Data
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Creating subjective and objective sentence classifiers from unannotated texts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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As an important application in text mining and social media, sentiment detection has aroused more and more research interests, due to the expanding volume of available online information such as microblogging messages and review comments. Many machine learning methods have been proposed for sentiment detection. As a branch of machine learning, transfer learning is an important technique that tries to transfer knowledge from one domain to another one. When applied to sentiment detection, existing transfer learning methods employ articles with human labeled sentiments from other domains to help the sentiment detection on a target domain. Although most existing transfer learning methods are devoted to handle the data distribution difference between different domains, they only resort to some approximation methods, which may introduce some unnecessary biases. Furthermore, the popular assumption of existing transfer learning techniques on conditional probability is often too strong for practical applications. In this paper, we propose a novel method to model the distribution difference between different domains in sentiment detection by directly modeling the underlying joint distributions for different domains. Some of the important properties of the proposed method, such as the convergence rate and time complexity, are analyzed. The experimental results on the product review dataset and the twitter dataset demonstrate the advantages of the proposed method over the state-of-the-art methods.