Polarity Classification of Public Health Opinions in Chinese
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
A machine learning approach to sentiment analysis in multilingual Web texts
Information Retrieval
Aspect-based sentence segmentation for sentiment summarization
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Automatic Extraction for Product Feature Words from Comments on the Web
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
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We investigate the problem of identifying both product properties and opinion words for sentences in a unified process when only a much small labeled corpus is available. Naive Bayesian method is used in this process. Specifically, considering the fact that product properties and opinion words usually co-occur with high frequency in product review articles, a crosstraining method is proposed to bootstrap both of them, in which the two sub-tasks are boosted by each other iteratively. Experiment results show that with a much small labeled corpus cross-training could produce both product properties and opinion words which are very close to what Naive Bayesian Classifiers could do with a large labeled corpus..