  Currently, there are more than a dozen Russian-language corpora for sentiment
analysis, differing in the source of the texts, domain, size, number and ratio
of sentiment classes, and annotation method. This work examines publicly
available Russian-language corpora, presents their qualitative and quantitative
characteristics, which make it possible to get an idea of the current landscape
of the corpora for sentiment analysis. The ranking of corpora by annotation
quality is proposed, which can be useful when choosing corpora for training and
testing. The influence of the training dataset on the performance of sentiment
analysis is investigated based on the use of the deep neural network model
BERT. The experiments with review corpora allow us to conclude that on average
the quality of models increases with an increase in the number of training
corpora. For the first time, quality scores were obtained for the corpus of
reviews of ROMIP seminars based on the BERT model. Also, the study proposes the
task of the building a universal model for sentiment analysis.
