TCKGCN: Graph convolutional network for aspect-based sentiment analysis with three-channel knowledge fusion

Abstract

At present, most aspect-based sentiment analysis studies addresses the syntactic dependency problem of review statements by building syntactic dependency graphs. However, this approach is insensitive to syntactic structural information and susceptible to noise interference. Existing fusion methods are unable to effectively capture the complementary relationships between information, leading to large and less efficient model structures. To tackle these issues, we introduce a unique and original scheme that fuses semantic information, ConceptNet conceptual knowledge, and SenticNet affective knowledge, intending to improve the capability of the model to capture sentence features. Specifically, the process involves four steps: first, a semantic attention mechanism is constructed as the first channel to parse the semantic relationship within the context; second, the expressive meaning of the aspects is enhanced with concept mapping as the second channel, with concepts injected into aspectual words; third, the representation ability of sentence dependency graphs is strengthened through adding the affective knowledge of SenticNet as a third channel. Subsequently, the sensitivity of the model to affective information is increased, and finally, the optimization of aspect and context coordination is strengthened by integrating an interactive attention mechanism. The overall findings underscore the efficacy of the model, showing accuracy improvements of 1.05 % and 2.80 % on the Twitter and Lap14 datasets, respectively, compared to state-of-the-art models. Moreover the macro-F1 scores showed significant improvements of 1.99 % and 1.61 %, respectively, enabling more effective capturing of aspect-specific sentiment expressions.