Dual Channel Graph Neural Network Enhanced by External Affective Knowledge for Aspect Level Sentiment Analysis

Aspect-level sentiment analysis is a prominent technology in natural language processing (NLP) that analyzes the sentiment polarity of target words in a text. Despite its long history of development, current methods still have some shortcomings. Mainly, they lack the integration of external affective knowledge, which is crucial for allocating attention to aspect-related words in syntactic and semantic information processing. Additionally, the synergy between syntactic and semantic information is often neglected, with most approaches focusing on only one dimension. To address these issues, we propose a knowledge-enhanced dual-channel graph neural network. Our model incorporates external affective knowledge into both the semantic and syntactic channels in different ways, then utilizes a dynamic attention mechanism to fuse information from these channels. We conducted experiments on Semeval2014, 2015, and 2016 datasets, and the results showed significant improvements compared to existing methods. Our approach bridges the gaps in current techniques and enhances performance in aspect-level sentiment analysis.