  The performance of sentiment analysis methods has greatly increased in recent
years. This is due to the use of various models based on the Transformer
architecture, in particular BERT. However, deep neural network models are
difficult to train and poorly interpretable. An alternative approach is
rule-based methods using sentiment lexicons. They are fast, require no
training, and are well interpreted. But recently, due to the widespread use of
deep learning, lexicon-based methods have receded into the background. The
purpose of the article is to study the performance of the SO-CAL and
SentiStrength lexicon-based methods, adapted for the Russian language. We have
tested these methods, as well as the RuBERT neural network model, on 16 text
corpora and have analyzed their results. RuBERT outperforms both lexicon-based
methods on average, but SO-CAL surpasses RuBERT for four corpora out of 16.
