Mitigating unreliable syntactic dependencies in aspect-based sentiment analysis via sentiment-aware graph anomaly detection and mix convolution.
Aspect-Based Sentiment Analysis (ABSA) aims to determine the sentiment polarity expressed toward specific aspect terms within a sentence. Existing approaches often rely on syntactic dependency parse graphs to model the relationship between aspect terms and opinion words. Despite recent progress, two key challenges remain unresolved. First, dependency parse graphs often contain task-irrelevant yet syntactically valid edges that can mislead sentiment reasoning. Second, meaningful sentiment relations between aspect terms and opinion words frequently span multiple dependency edges, making it difficult for models to capture long-range semantic interactions. To address these challenges, we propose a Sentiment-Aware Graph Anomaly detection and Mix convolution (SAGAM) model. The sentiment-aware graph anomaly detection framework integrates graph anomaly detection with a sentiment consistency evaluation mechanism to eliminate misleading dependencies and preserve key sentiment information from the original graph. To further capture long-range dependencies between aspect terms and opinion words, the mix convolution module combines multi-hop message propagation with edge feature integration, enabling the model to capture both local and distant semantic interactions. Extensive experiments on three widely used ABSA benchmark datasets demonstrate that SAGAM consistently outperforms strong baselines in both accuracy and macro-F1. Notably, the proposed sentiment-aware graph anomaly detection framework can be seamlessly incorporated into other graph-based ABSA models to enhance performance and robustness.