  In this study, we test transfer learning approach on Russian sentiment
benchmark datasets using additional train sample created with distant
supervision technique. We compare several variants of combining additional data
with benchmark train samples. The best results were achieved using three-step
approach of sequential training on general, thematic and original train
samples. For most datasets, the results were improved by more than 3% to the
current state-of-the-art methods. The BERT-NLI model treating sentiment
classification problem as a natural language inference task reached the human
level of sentiment analysis on one of the datasets.
