Machine Learning
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Minimum error rate training in statistical machine translation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
Automatic identification of pro and con reasons in online reviews
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
How effective is Google's translation service in search?
Communications of the ACM - A View of Parallel Computing
Multilingual subjectivity analysis using machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Findings of the 2009 workshop on statistical machine translation
StatMT '09 Proceedings of the Fourth Workshop on Statistical Machine Translation
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
Evaluating multilanguage-comparability of subjectivity analysis systems
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Cross-language document summarization based on machine translation quality prediction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Multilingual subjectivity: are more languages better?
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Creating sentiment dictionaries via triangulation
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
Multilingual Sentiment Analysis Using Latent Semantic Indexing and Machine Learning
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Learning to translate: a statistical and computational analysis
Advances in Artificial Intelligence
ONTS: "optima" news translation system
EACL '12 Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics
Machine translation for multilingual summary content evaluation
Proceedings of Workshop on Evaluation Metrics and System Comparison for Automatic Summarization
Improving AMBER, an MT evaluation metric
WMT '12 Proceedings of the Seventh Workshop on Statistical Machine Translation
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Sentiment analysis is the natural language processing task dealing with sentiment detection and classification from texts. In recent years, due to the growth in the quantity and fast spreading of user-generated contents online and the impact such information has on events, people and companies worldwide, this task has been approached in an important body of research in the field. Despite different methods having been proposed for distinct types of text, the research community has concentrated less on developing methods for languages other than English. In the above-mentioned context, the present work studies the possibility to employ machine translation systems and supervised methods to build models able to detect and classify sentiment in languages for which less/no resources are available for this task when compared to English, stressing upon the impact of translation quality on the sentiment classification performance. Our extensive evaluation scenarios show that machine translation systems are approaching a good level of maturity and that they can, in combination to appropriate machine learning algorithms and carefully chosen features, be used to build sentiment analysis systems that can obtain comparable performances to the one obtained for English.