Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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 Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Co-clustering based classification for out-of-domain documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Latent space domain transfer between high dimensional overlapping distributions
Proceedings of the 18th international conference on World wide web
EigenTransfer: a unified framework for transfer learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Extracting discriminative concepts for domain adaptation in text mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge transformation for cross-domain sentiment classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
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
Translingual document representations from discriminative projections
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Adaptive co-training SVM for sentiment classification on tweets
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Sentiment classification is becoming attractive in recent years because of its potential commercial applications. It exploits supervised learning methods to learn the classifiers from the annotated training documents. The challenge in sentiment classification lies in that the sentiment domains are diverse, heterogeneous and fast-growing. The classifiers trained on one domain (source domain) could not classify a document from another domain (target domain). The domain adaptation technique is to address the problem by making use of labeled samples in the source domain, and unlabeled samples in the target domain. This paper presents a new solution, a cross-domain topic indexing (CDTI) method, with which a common semantic space is found from the prior between-domain term correspondences and the term co-occurrences in the cross-domain documents. These observations are characterized with the mixture model in CDTI, with each component being a possible topic shared by the source and target domains. Such common topics are found to index the cross-domain content. We evaluate the algorithms on a multi-domain sentiment classification task, which shows that CDTI outperforms the state-of-the-art domain adaptation method, i.e. spectral feature alignment (SFA), and the traditional latent semantic indexing method.