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This paper describes our participation at tasks 10 (sub-task B, Message Polarity Classification) and 11 task (Sentiment Analysis of Figurative Language in Twitter) of Semeval2015. We describe the Support Vector Machine system we used in this competition. We also present the relevant feature set that we take into account in our models. Finally, we show the results we obtained in this competition and some conclusions.

ELiRF: A SVM Approach for SA tasks in Twitter at SemEval-2015