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
A novel refinement approach for text categorization
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
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
Comparative experiments on sentiment classification for online product reviews
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Building domain-oriented sentiment lexicon by improved information bottleneck
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the third ACM international conference on Web search and data mining
A random walk algorithm for automatic construction of domain-oriented sentiment lexicon
Expert Systems with Applications: An International Journal
Bilingual co-training for sentiment classification of chinese product reviews
Computational Linguistics
Power walk: revisiting the random surfer
Proceedings of the 18th Australasian Document Computing Symposium
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Sentiment classification is attracting more and more attention because of its great benefits to social and human life. Usually supervised classification approaches perform well in sentiment classification, but the performance decreases sharply when transferred from one domain to another domain. In this paper, we propose an approach, SentiRank, which integrates the sentiment orientations of the documents into the graph-ranking algorithm for cross-domain sentiment classification. We apply the graph-ranking algorithm using the accurate labels of old-domain documents as well as the “pseudo” labels of new-domain documents, and investigate their relative importance for cross-domain sentiment classification. The experiment results indicate that the proposed algorithm could improve the performance of cross-domain sentiment classification dramatically.