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The emergence of online social networks in the past few years has generated an enormous amount of information about potentially any subject. Valuable data containing users' opinions and thoughts are available on those repositories and several sentiment analysis techniques have been proposed that address the problem of understanding the opinion orientation of the users' postings. In this paper, we take a different perspective to the problem through a user centric approach, which uses a graph to model users (and their postings) and applies link mining techniques to infer opinions of users. Preliminary experiments on a Twitter corpus have shown promising results.