Fbm-yahoo! at replab 2012

Abstract. This paper describes FBM-Yahoo!’s participation in the pro-filing task of RepLab 2012, which aims at determining whether a given tweet is related to a specific company and, in if this being the case, whether it contains a positive or negative statement related to the com-pany’s reputation or not. We addressed both problems (ambiguity and polarity reputation) using Support Vector Machines (SVM) classifiers and lexicon-based techniques, building automatically company profiles and bootstrapping background data. Concretely, for the ambiguity task we employed a linear SVM classifier with a token-based representation of relevant and irrelevant information extracted from the tweets and Free-base resources. With respect to polarity classification, we combined SVM lexicon-based approaches with bootstrapping in order to determine the final polarity label of a tweet.