Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis

• We study the possibility to employ Machine Translation (MT) systems and supervised methods for multilingual sentiment analysis.
• Experiments are done for English, German, Spanish and French.
• We use three MT systems – Google, Bing and Moses –, different supervised learning algorithms and various types of features.
• We show how meta-classifiers can be employed to mitigate the noise introduced by translation.
• Our extensive evaluations show that MT systems can be used for multilingual sentiment analysis.

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.