Deep Transfer Learning Baselines for Sentiment Analysis in Russian

Recently, transfer learning from pre-trained language models has proven to be effective in a variety of natural language processing tasks, including sentiment analysis. This paper aims at identifying deep transfer learning baselines for sentiment analysis in Russian. Firstly, we identified the most used publicly available sentiment analysis datasets in Russian and recent language models which officially support the Russian language. Secondly, we fine-tuned Multilingual Bidirectional Encoder Representations from Transformers (BERT), RuBERT, and two versions of the Multilingual Universal Sentence Encoder and obtained strong, or even new, state-of-the-art results on seven sentiment datasets in Russian: SentRuEval-2016, SentiRuEval-2015, RuTweetCorp, RuSentiment, LINIS Crowd, and Kaggle Russian News Dataset, and RuReviews. Lastly, we made fine-tuned models publicly available for the research community.