ExtRA++: A Conceptual Architecture for a Deep Learning System for Aspect-Based Sentiment Analysis in User Reviews

Abstract: 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.

G. Kanev and I. Valova. “ExtRA++: A Conceptual Architecture for a Deep Learning System for Aspect-Based Sentiment Analysis in User Reviews”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.5 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170503

@article{Kanev2026,
title = {ExtRA++: A Conceptual Architecture for a Deep Learning System for Aspect-Based Sentiment Analysis in User Reviews},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170503},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170503},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {5},
author = {G. Kanev and I. Valova}
}