Identifying and analyzing judgment opinions
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Learning MultiLinguistic Knowledge for Opinion Analysis
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Multilingual subjectivity analysis using machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Co-training for cross-lingual sentiment classification
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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This paper presents two instance-level transfer learning based algorithms for cross lingual opinion analysis by transferring useful translated opinion examples from other languages as the supplementary training data for improving the opinion classifier in target language. Starting from the union of small training data in target language and large translated examples in other languages, the Transfer AdaBoost algorithm is applied to iteratively reduce the influence of low quality translated examples. Alternatively, starting only from the training data in target language, the Transfer Self-training algorithm is designed to iteratively select high quality translated examples to enrich the training data set. These two algorithms are applied to sentence- and document-level cross lingual opinion analysis tasks, respectively. The evaluations show that these algorithms effectively improve the opinion analysis by exploiting small target language training data and large cross lingual training data.