Knowledge Graph Enhanced Aspect-Level Sentiment Analysis

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.

@article{sharma2025_2312.10048,
  title={ Knowledge Graph Enhanced Aspect-Level Sentiment Analysis },
  author={ Kavita Sharma and Ritu Patel and Sunita Iyer },
  journal={arXiv preprint arXiv:2312.10048},
  year={ 2025 }
}