Aspect-Level Sentiment Analysis Based on Lite Bidirectional Encoder Representations From Transformers and Graph Attention Networks

Aspect-level sentiment analysis is a critical component of sentiment analysis, aiming to determine the sentiment polarity associated with specific aspect words. However, existing methodologies have limitations in effectively managing aspect-level sentiment analysis. These limitations include insufficient utilization of syntactic information and an inability to precisely capture the contextual nuances surrounding aspect words. To address these issues, we propose an Aspect-Oriented Graph Attention Network (AOGAT) model. This model incorporates syntactic information to generate dynamic word vectors through the pre-trained model ALBERT and combines a graph attention network with BiGRU to capture both syntactic and semantic features. Additionally, the model introduces an aspect-focused attention mechanism to retrieve features related to aspect words and integrates the generated representations for sentiment classification. Our experiments on three datasets demonstrate that the AOGAT model outperforms traditional models.