AWA-GCN: Enhancing Chinese sentiment analysis with a novel GCN model for Triplet and Quadruplet Extraction at SIGHAN 2024 dimABSA Task

Abstract

This study presents the AWA-GCN (Attention-Weighted Affine with Graph Convolutional Networks) model, a new method designed exclusively for the SIGHAN 2024 Chinese Dimensional Aspect-Based Sentiment Analysis (dimABSA) Task. This test requires participants to identify sentiment triples and sentiment quadruples from textual data. The data comprises aspects, sentiment views, categories, and intensities across valence-arousal dimensions. Our model differs from existing models by utilizing a complex multi-layer attention mechanism within a GCN architecture, instead of relying on pipeline techniques or Grid Tagging Schemes (GTS). This design successfully captures the intricate interconnections among many sentiment components. The AWA-GCN model is very innovative in its ability to effectively handle quadruple extraction, which is a complex problem that has not been handled by traditional methods. Our approach enhances the understanding of aspect-sentiment interactions by including sophisticated techniques like word-level attention and semantic graph representations. The AWA- GCN model outperforms previous baselines in precision, recall, and F1 score on the dimABSA dataset, as demonstrated by empirical assessments. Additionally, the model exhibits significant enhancements in capturing the dimensional features of sentiment expressions. The results validate the models exceptional ability to handle the complex nuances necessary for successful sentiment analysis in simple Chinese.