Label-guided data augmentation for cross-domain aspect-based sentiment analysis via enhanced affinity fusion

Abstract

Aspect-Based Sentiment Analysis (ABSA) aims to identify opinion targets and determine the sentiment polarity expressed toward each aspect, which requires fine-grained modeling of aspect–opinion relations. Despite recent advances, cross-domain ABSA remains challenging due to structural mismatches across domains and the scarcity of high-quality labeled data in target domains. Existing methods often struggle to jointly address relational modeling errors and data sparsity, particularly under low-resource and cross-domain settings. To tackle these challenges, we propose a structure- and data-co-enhanced framework for cross-domain ABSA. At the model level, we introduce an Enhanced Affinity Fusion (EAF) module that explicitly strengthens aspect–opinion relational modeling by selectively integrating complementary attention mechanisms. Specifically, EAF combines biaffine attention to capture second-order interactions with syntax-aware attention to inject structural inductive bias, enabling robust modeling of long-distance dependencies without introducing excessive architectural complexity. At the data level, we propose Label-Guided Data Amplification (LGDA), which enhances supervision diversity and domain robustness through label-driven text expansion, hard sample mining, and domain-adaptive sampling. By jointly enhancing structural representation learning and training data supervision, the proposed framework effectively alleviates both aspect–opinion mismatches and cross-domain data sparsity. Extensive experiments on benchmark ABSA datasets demonstrate that our approach consistently outperforms strong baselines and achieves state-of-the-art performance in cross-domain scenarios. Ablation studies further validate the complementary contributions of EAF and LGDA.