This paper proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by seven lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine. The system has been evaluated on the standard tweet sets used in the SemEval 2015 competition, obtaining results that, in most cases, outperform those of the state-of-the-art sentiment analysis systems.
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