Multilingual Entity-Centered Sentiment Analysis Evaluated by Parallel Corpora

We propose the creation and use of a multilingual parallel news corpus annotated with opinion towards entities, produced by projecting sentiment annotation from one language to several others. The objective is to save annotation time for development and evaluation purposes, and to guarantee comparability of opinion mining evaluation results across languages. By creating this resource, we answered the question whether sentiment is consistently translated across languages so that projection can actually be an option. We describe our approach to multilingual sentiment analysis and show its performance in 7 languages of the parallel corpus.