Knowledge Graph-Driven Approach in Aspect-Based Sentiment Analysis: Exploring the Impact of Embedding Techniques

Despite the high performance of the existing embedding approaches for Aspect-Based Sentiment Analysis (ABSA), such as Word2Vec and GloVe, they still have several limitations, mainly in contextual understanding and relational insights of natural language, especially in complex and long sentences. This paper presents a novel approach that enhances ABSA by integrating knowledge graphs into a transformer model, where the graphs are automatically built from raw text without requiring external resources, making the system adaptable and fully data-driven. Our approach allows a better understanding of context and relationships between entities, by combining of the contextual understanding of BERT with the relational insights provided by Node2Vec, a graph-based embedding technique. In this paper, we benchmark our hybrid embedding technique with the existing state-of-the-art embedding techniques. Specifically, we compare traditional embeddings, such as Word2Vec and GloVe, against BERT for textual input, while also exploring Word2Vec and Node2Vec for graph-based embeddings. Our experiments demonstrate that combining BERT’s deep contextual embeddings with the structural insights of Node2Vec leads to promising improvements in sentiment classification performance. Our model achieved 98% accuracy on SemEval2015 Restaurant dataset. These results demonstrate that integrating both contextual and relational information significantly enhances the performance of ABSA models, thereby making them more effective at capturing nuanced sentiment relationships. The proposed model’s modular design also allows flexible integration of alternative embeddings or graph configurations, making it suitable for broader sentiment analysis applications beyond the benchmark datasets.