Aspect-Based Sentiment Analysis (ABSA) aims to identify opinion targets within textual reviews and determine the sentiment polarity associated with each target. Although transformer-based models have significantly improved contextual understanding in sentiment analysis, they remain limited in explicitly modeling structured knowledge and token-level dependencies. This study presents ExtRA++ (Enhanced Extractive Review Analysis), a conceptual deep learning architecture for fine-grained aspect-based sentiment analysis in user-generated reviews. The proposed framework integrates four complementary components: BERT-based contextual semantic modeling, adaptive external knowledge integration through Wikidata embeddings, graph-based structural reasoning using Graph Attention Networks (GATs), and sequence-consistent aspect extraction through Conditional Random Fields (CRFs) combined with aspect-aware sentiment classification. Unlike transformer-only approaches, ExtRA++ is designed as a modular systems-level architecture that combines contextual semantics, factual grounding, structural token interactions, and structured decoding within a unified framework.
Kanev et al. (2026) studied this question.