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We present an approach for tackling the Sentiment Analysis problem in SemEval 2015. The approach is based on the use of a cooccurrence graph to represent existing relationships among terms in a document with the aim of using centrality measures to extract the most representative words that express the sentiment. These words are then used in a supervised learning algorithm as features to obtain the polarity of unknown documents. The best results obtained for the different datasets are: 77.76%doi:10.18653/v1/s15-2093 dblp:conf/semeval/CastilloCVBS15 fatcat:u4ifmx2j3zaa5c5lrtvjaq63i4