  Named Entity Sentiment analysis (NESA) is one of the most actively developing
application domains in Natural Language Processing (NLP). Social media NESA is
a significant field of opinion analysis since detecting and tracking sentiment
trends in the news flow is crucial for building various analytical systems and
monitoring the media image of specific people or companies. In this paper, we
study different transformers-based solutions NESA in RuSentNE-23 evaluation.
Despite the effectiveness of the BERT-like models, they can still struggle with
certain challenges, such as overfitting, which appeared to be the main obstacle
in achieving high accuracy on the RuSentNE-23 data. We present several
approaches to overcome this problem, among which there is a novel technique of
additional pass over given data with masked entity before making the final
prediction so that we can combine logits from the model when it knows the exact
entity it predicts sentiment for and when it does not. Utilizing this
technique, we ensemble multiple BERT- like models trained on different subsets
of data to improve overall performance. Our proposed model achieves the best
result on RuSentNE-23 evaluation data and demonstrates improved consistency in
entity-level sentiment analysis.
