Association for Computing Machinery
Recent advancements in machine translation foster an interest of its use in sentiment analysis. In this paper, the authors investigate prospects and limitations of machine translation in sentiment analysis for cross-lingual polarity detection task. They focus on improving classification accuracy in a cross-lingual setting where they have available labeled training instances about particular domain in different languages. They 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.