In these preliminary experiments, the best-performing model (NNC) reached an F1 of 0.637 for the positive class vs. the rest and F1 of 0.550 for negative vs. the rest in binary classification.
lexical features have time and again shown inferior performance compared to the supervised learning approaches using annotated data (Gombar et al., 2017), and lack of such a resource severely limits sentiment analysis applications for Russian.