Subjectivity analysis has received increasing attention in natural language processing field. Most of the subjectivity analysis works however are conducted on single languages. In this paper, we propose to perform multilingual subjectivity analysis by combining multi-view learning and AdaBoost techniques. We aim to show that by boosting multi-view classifiers we can develop more effective multilingual subjectivity analysis tools for new languages as well as increase the classification performance for English data. We empirically evaluate our two multi-view AdaBoost approaches on the multilingual MPQA dataset. The experimental results show the multi-view AdaBoost approaches significantly outperform existing monolingual and multilingual methods.

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24th International Conference on Computational Linguistics, COLING 2012
School of Computer Science

Xiao, M. (Min), & Guo, Y. (2012). Multi-view adaboost for multilingual subjectivity analysis. In 24th International Conference on Computational Linguistics - Proceedings of COLING 2012: Technical Papers (pp. 2851–2866).