Aspect-Based Sentiment Analysis (ABSA) predicts sentiment polarity for specific aspect terms, a task made difficult by conflicting sentiments across aspects and the sparse context of short texts. Prior graph-based approaches model only pairwise dependencies, forcing them to construct multiple graphs for different relational views. These introduce redundancy, parameter overhead, and error propagation during fusion, limiting robustness in short-text, low-resource settings. We present HyperABSA, a dynamic hypergraph framework that induces aspect-opinion structures through sample-specific hierarchical clustering. To construct these hyperedges, we introduce a novel acceleration-fallback cutoff for hierarchical clustering, which adaptively determines the level of granularity. Experiments on three benchmarks (Lap14, Rest14, MAMS) show consistent improvements over strong graph baselines, with substantial gains when paired with RoBERTa backbones. These results position dynamic hypergraph construction as an efficient, powerful alternative for ABSA, with potential extensions to other short-text NLP tasks.

翻译：基于方面的情感分析（ABSA）旨在预测特定方面术语的情感极性，这一任务因不同方面间的情感冲突以及短文本的稀疏上下文而变得困难。以往的基于图的方法仅建模成对依赖关系，迫使它们为不同的关系视图构建多个图。这些方法引入了冗余、参数开销以及融合过程中的误差传播，限制了在短文本、低资源设置下的鲁棒性。我们提出了HyperABSA，一种动态超图框架，通过样本特定的层次聚类来推导方面-意见结构。为了构建这些超边，我们引入了一种新颖的层次聚类加速-回退截止方法，该方法自适应地确定粒度级别。在三个基准数据集（Lap14、Rest14、MAMS）上的实验表明，与强大的图基线相比，该方法取得了持续改进，特别是与RoBERTa骨干网络结合时获得了显著提升。这些结果将动态超图构建定位为ABSA的一种高效、强大的替代方案，并具有扩展到其他短文本自然语言处理任务的潜力。