  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.
