From Graphs to Hypergraphs: Enhancing Aspect-Term Sentiment Analysis via Multi-Level Relational Modeling

Abstract

Aspect-Term Sentiment Analysis (ATSA) aims to predict sentiment polarity for specific aspect terms, a task complicated by conflicting sentiments and limited context in short texts. Existing graph-based approaches rely on pre-defined pairwise structures to capture different linguistic views. However, this leads to two key limitations: (1) their pairwise formulation often requires multiple graphs to improve expressive capacity, and (2) their reliance on predefined parsers or heuristic graph construction limits adaptability to sentence-specific sentiment composition. We propose HyperATSA, a dynamic hypergraph framework that overcomes these limitations through a single instance-specific hypergraph constructed directly from contextual token representations. Hyperedges are dynamically induced via Hierarchical Agglomerative Clustering (HAC) over token embeddings, where an acceleration-based cutoff identifies sentence-specific semantic groupings and enables adaptive hypergraph construction. Experiments on Lap14, Rest14, and MAMS demonstrate consistent improvements over strong graph-based baselines, suggesting that hypergraph-based relational modeling generalizes effectively to short-text sentiment composition.