In this paper, we propose a novel method to enhance sentiment analysis by addressing the challenge of context-specific word meanings. It combines the advantages of a BERT model with a knowledge graph based synonym data. This synergy leverages a dynamic attention mechanism to develop a knowledge-driven state vector. For classifying sentiments linked to specific aspects, the approach constructs a memory bank integrating positional data. The data are then analyzed using a DCGRU to pinpoint sentiment characteristics related to specific aspect terms. Experiments on three widely used datasets demonstrate the superior performance of our method in sentiment classification.

翻译：本文提出一种新颖方法，通过解决上下文特定词义的挑战来增强情感分析。该方法结合了BERT模型与基于知识图谱的同义词数据的优势，利用动态注意力机制构建知识驱动的状态向量。为分类与特定方面相关的情感，该方法构建了整合位置数据的记忆库，随后使用DCGRU对数据进行分析，以识别与特定方面术语相关的情感特征。在三个广泛使用的数据集上的实验表明，该方法在情感分类中具有卓越性能。