Multi-source knowledge fusion model for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to give the corresponding sentiment polarity for specific aspects that appear in review statements. Most existing aspect-based sentiment analysis methods relying on deep learning focus on mining the semantics and syntax of review statements, often ignoring the conceptual knowledge and sentiment degree information that may be involved in the review statements. To address this problem, a neural network model incorporating multi-source knowledge. It reveals the structural framework of sentences through syntactic dependencies, captures semantic connections between words through word co-occurrence, and embeds emotional networks and concept graphs to provide emotional and background knowledge for the model, and coordinated optimization of the contextual and evaluative aspects of review statements was realized through a dual-interaction attention model. Experimental results on four public datasets show that the model achieves better performance than existing models, with accuracy reaching 75.00%, 77.90%, 81.55%, and 90.10%, respectively, all of which were improved compared to the benchmark model. This achievement not only verifies the effectiveness of multi-source knowledge fusion in ABSA tasks, but also provides new ideas and methods for future research. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.