The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
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
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
Cross lingual adaptation: an experiment on sentiment classifications
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
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Recently there has been a lot of interest in Cross Language Sentiment Analysis (CLSA) using Machine Translation (MT) to facilitate Sentiment Analysis in resource deprived languages. The idea is to use the annotated resources of one language (say, L1) for performing Sentiment Analysis in another language (say, L2) which does not have annotated resources. The success of such a scheme crucially depends on the availability of a MT system between L1 and L2. We argue that such a strategy ignores the fact that a Machine Translation system is much more demanding in terms of resources than a Sentiment Analysis engine. Moreover, these approaches fail to take into account the divergence in the expression of sentiments across languages. We provide strong experimental evidence to prove that even the best of such systems do not outperform a system trained using only a few polarity annotated documents in the target language. Having a very large number of documents in L1 also does not help because most Machine Learning approaches converge (or reach a plateau) after a certain training size (as demonstrated by our results). Based on our study, we take the stand that languages which have a genuine need for a Sentiment Analysis engine should focus on collecting a few polarity annotated documents in their language instead of relying on CLSA.