Resource Creation and Evaluation for Multilingual Sentiment Analysis in Social Media Texts
Alex Balahur, ra, Marco Turchi, Ralf Steinberger, Jose-Manuel Perea-Ortega, Guillaume Jacquet, Dilek K{\"u}{\c{c}}{\"u}k, Vanni Zavarella, Adil El Ghali
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This paper presents an evaluation of the use of machine translation to obtain and employ data for training multilingual sentiment classifiers. We show that the use of machine translated data obtained similar results as the use of native-speaker translations of the same data. Additionally, our evaluations pinpoint to the fact that the use of multilingual data, including that obtained through machine translation, leads to improved results in sentiment classification. Finally, we show that the performance of the sentiment classifiers built on machine translated data can be improved using original data from the target language and that even a small amount of such texts can lead to significant growth in the classification performance.