<![CDATA[Aspect-Based Sentiment Analysis (ABSA) is a critical natural language processing task aimed at identifying specific aspects within text and determining the sentiment polarity toward each aspect. Transformer-based models, particularly BERT and its variants, have demonstrated significant advances in ABSA through powerful contextual representations. However, challenges in capturing target-specific context and managing inter-subtask dependencies remain. This Systematic Literature Review (SLR) identifies, evaluates, and synthesizes current research on context-aware transformer models for ABSA, with emphasis on context-aware mechanisms, multi-task learning approaches, and BERT-family models. Following the PRISMA 2020 protocol, a structured search was conducted on the Scopus database using three Boolean queries, yielding 851 initial records. After deduplication (n=70), title/abstract screening (n=554 excluded), retrieval (n=147 not retrieved), and full-text eligibility assessment (n=48 excluded), 32 studies were included for synthesis. Three primary model categories were identified: (1) BERT baselines establishing strong end-to-end ABSA performance; (2) context-aware variants employing context-guided attention (CG-BERT, QACG-BERT, LCF-ATEPC, cascade models); and (3) multi-task transformers (BERT-MTL, RoBERTa-MTL, MTL-AraBERT, SABKG, MLEGCN) handling ABSA subtasks jointly. Reported F1-scores ranged from 50–89% across SemEval-2014/2015/2016 and domain-specific datasets. ntext-aware and multi-task transformer models represent the state of the art in ABSA. Open challenges include implicit aspect handling, cross-domain generalization, model efficiency, and evaluation of large generative language models (LLMs) for fine-grained sentiment tasks.]]>
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