In this paper, we present a method that exploits syntactic dependencies for topic-oriented sentiment analysis in microposts. The proposed solution is based on supervised text classification (decision trees in particular) and freely-available polarity lexicons in order to identify the relevant dependencies in each sentence by detecting the correct attachment points for the polarity words. Our experiments are based on the data from the Semantic Evaluation Exercise 2015 (SemEval-2015), task 10, subtask C. The dependency parser that we used is adapted to this kind of text. Our classifier that combines both topic- and sentence-level features obtained very good results.