The paper describes our approach to the task of sentiment analysis of tweets within SentiRuEval—an open evaluation of sentiment analysis systems for the Russian language. We took part in the task of object-oriented sentiment analysis of Russian tweets concerning two types of organizations: banks and telecommunications companies. On both datasets, the participants were required to perform a three-way classification of tweets: positive, negative or neutral. We used various statistical methods as basis for our machine learning algorithms and checked which features would provide the best results. Syntactic relations proved to be a crucial feature to any statistical method evaluated, but SVM-based classification performed better than the others. Normalized words are another important feature for the algorithm. The evaluation revealed that our method proved to be rather successful: we scored the first in three out of four evaluation measures.