Aspect-Based Sentiment Analysis (ABSA) has become a critical tool for extracting fine-grained insights from user opinions. This paper introduces DeGF-ABSA (DeBERTa-Gated Fusion for Aspect-Based Sentiment Analysis), a novel architecture that addresses key limitations in existing approaches by dynamically balancing global contextual features and aspect features. Unlike traditional methods that rigidly combine context and aspect representations—or transformer-based models lacking explicit mechanisms to disentangle aspect-specific signals—DeGF-ABSA leverages DeBERTa’s disentangled attention mechanism, which excels at modeling positional dependencies in technical texts, paired with a gated fusion layer. This layer adaptively weights the contributions of the context features that come from the CLS token, and the aspect-specific features come from the mean of aspect tokens. This helps in accurately determining the sentiment in complex sentences. Experiments on SemEval 2014 datasets achieve state-of-the-art results: 86.68% accuracy (84.50% F1) for Laptops and 91.43% accuracy (86.83% F1) for Restaurants.Cross-domain generalization is critical for aspect-based sentiment analysis, as domain-specific aspects and vocabulary vary significantly. Sentiment expressions also differ across domains, such as 'delicious' for food reviews versus 'fast performance' for electronics, requiring adaptable models to capture contextual nuances. Evaluating the DeGF-ABSA model's performance on datasets from domains beyond laptops and restaurants would provide valuable insights into its ability to generalize and its potential for broader applicability.
Mohammadiani et al. (Thu,) studied this question.