From Context to Aspects: LLM-Based Implicit Aspect Extraction with Paraphrased Input and Knowledge Graph Support

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While aspect-based sentiment analysis (ABSA) has made significant progress in the identification of explicit opinion targets, the more challenging case of implicit aspects remains insufficiently studied. Implicit aspect extraction is particularly challenging, as it relies on contextual and semantic cues and requires systems to infer what reviewers mean rather than what they state explicitly. A four-component hybrid pipeline is proposed for explicit and implicit aspect extraction, formulating the task as controlled text generation. The pipeline combines (i) a fine-tuned decoder-only large language model as a generative baseline, (ii) an iterative residual generation strategy that recovers multiple aspects through successive masked generation passes, (iii) paraphrase-based input transformation to broaden the contextual signal, and (iv) domain-specific knowledge graphs activated by linguistic signals to infer implicit aspects. The novelty lies not in the individual components themselves but in their principled orchestration and the linguistically motivated gating logic governing the activation of each stage. Extensive experiments are conducted on eight benchmark ABSA datasets spanning both English and Arabic: SemEval-2014, SemEval-2015, SemEval-2016, ACOS, and M-ABSA for English; and SemEval-2016, HAAD, and M-ABSA for Arabic. The proposed solution outperforms strong baseline methods and recent state-of-the-art models on English datasets, with F1-scores of 0.8533, 0.713, 0.7859, 0.793, and 0.664, respectively. On Arabic datasets, the best-performing configurations achieve F1-scores of 0.7632, 0.4765, and 0.7656 on SemEval-2016, HAAD, and M-ABSA, respectively, with the knowledge-graph component providing consistent and statistically significant gains for implicit aspect identification in both languages. These results demonstrate the effectiveness of generative modeling, iterative generation, paraphrasing, and structured knowledge for aspect extraction and highlight the potential of the proposed approach for implicit aspect identification, in particular for morphologically rich languages such as Arabic, where annotated resources remain scarce.