ACL '05 Proceedings of the 43rd 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
Multilingual subjectivity analysis using machine translation
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
Multilingual subjectivity: are more languages better?
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Is machine translation ripe for cross-lingual sentiment classification?
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
SentiCorr: Multilingual Sentiment Analysis of Personal Correspondence
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Creating sentiment dictionaries via triangulation
Decision Support Systems
RBEM: a rule based approach to polarity detection
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
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Recent advancements in machine translation foster an interest of its use in sentiment analysis. In this paper, we investigate prospects and limitations of machine translation in sentiment analysis for cross-lingual polarity detection task. We focus on improving classification accuracy in a cross-lingual setting where we have available labeled training instances about particular domain in different languages. We experiment with movie review and product review datasets consisting of polar texts in English and Turkish. The results of the study show that expanding training size with new instances taken from another corpus does not necessarily increase classification accuracy. And this happens primarily not due to (not always accurate) machine translation, but because of the inherent differences in corpora between two subsets written in different languages. Similarly, in case of co-training classification with machine translation we observe from the results that accuracy improvement can be explained by semi-supervised learning with unlabeled data coming from the same domain, but not due to cross-language co-training itself. Our results also show that amount of artificial noise added by machine translation services does not hinder classifiers much in polarity detection task. However, it is important to distinguish the effect of machine translation from the effect of merging different cross-lingual data sources and that like in case of transfer learning we may need to search for ways to account for cross-lingual data distribution differences.