Adaptive contextual memory graph transformer with domain-adaptive knowledge graph for aspect-based sentiment analysis - Dubey, Gaurav; Dubey, Anil Kumar; Kaur, Kamaljit; Raj, Gaurav; Kumar, Parveen (Expert systems with applications, 2025)
Aspect-Based Sentiment Analysis (ABSA) faces critical challenges, including contextual variability, implicit sentiment inference, fluctuating sentiment intensity, and domain adaptation limitations. Traditional approaches struggle with accurately capturing domain-specific sentiment shifts, handling implicit aspects, and integrating multi-relational dependencies. To address these challenges, this study proposes the Adaptive Contextual Memory Graph Transformer (ACM-GT-ABSA), a novel framework that integrates dynamic knowledge graph construction, real-time contextual adaptation, and hierarchical sentiment representation. The Domain-Adaptive Knowledge Graph (DAKG) models domain-specific sentiment transitions and temporal variations, while the contextual memory embedding layer aligns historical sentiment trends with real-time data. A hierarchical graph encoder extracts multi-granular sentiment features, and implicit aspect detection employs contrastive learning and Semantic Role Labeling (SRL) to infer latent aspects. Additionally, hypergraph attention mechanisms model dependencies between primary and secondary intents for enhanced sentiment interpretation. Experimental evaluations on benchmark datasets (restaurant-15, restaurant-16, laptop-14 and twitter) demonstrate the model’s superior accuracy (up to 96.32%) and F1-score (88.36%), outperforming existing ABSA frameworks.