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.

翻译：命名实体情感分析（NESA）是自然语言处理（NLP）领域发展最活跃的应用方向之一。社交媒体NESA是意见分析的重要领域，因为检测和追踪新闻流中的情感趋势对于构建各类分析系统、监测特定人物或公司的媒体形象至关重要。本文研究了基于Transformer的多种解决方案在RuSentNE-23评测中处理NESA任务的表现。尽管BERT类模型具有有效性，但它们仍面临过拟合等挑战，这成为在RuSentNE-23数据上实现高精度的主要障碍。我们提出了多种克服该问题的方法，其中包括一种新技术：在最终预测前对给定数据以遮蔽实体进行额外遍历，从而结合模型在知晓所预测情感的具体实体时与不知晓该实体时的逻辑值。利用该技术，我们将多个在不同数据子集上训练的BERT类模型进行集成，以提升整体性能。所提模型在RuSentNE-23评测数据上取得最优结果，并在实体级情感分析中展现出更优的一致性。