Comparative Experiments Using Supervised Learning and Machine Translation 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 abovementioned 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. Our extensive evaluation scenarios show that MT 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.JRC.G.2-Global security and crisis managemen