Scholarlink

<p>Aspect-based sentiment analysis (ABSA) facilitates fine-grained opinion analysis by integrating sentiment polarity with precise aspects in textual reviews. Despite tremendous improvements, contemporary ABSA methodologies continue to struggle with issues such as precise multi-word aspect extraction, sensitivity to noisy data, inadequate domain generalization and fragmented approach to aspect extraction and sentiment classification. In order to tackle the above-mentioned constraints, this investigation proposes a novel multilevel fast point graph transformer network (MFP-GTN) that incorporates aspect extraction and sentiment classification in to an individual framework. The design combines a joint neural conditional random field (CRF) for accurate multi-word aspect recognition with a multilevel graph-based rapid point transformer which records both long-range contextual dependencies and local sentiment cues. A dual-phase metaheuristic combining hybrid giant trevally white shark optimization (HGTWSO) and enhanced salp swarm optimization (ESSO) is implemented for optimum feature selection and model tuning, in order to lower redundancy, enhance convergence, and improve generalization. Detailed trails across multiple benchmark datasets, including Flipkart cell phone reviews, restaurant reviews, financial sentiment datasets and consumer car ratings, demonstrate the effectiveness of MFP-GTN model. The model attains accuracies of 99.36%, 99.99%, 99.91%, and 99.91%, respectively, consistently outperforming baselines. The outcomes exhibit significant robust cross-domain efficiency, interpretability and scalability, and, proving it optimal for practical, application-oriented ABSA tasks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-domain reviews for aspect-based sentimental analysis using multilevel fast point graph transformer network

  • 期刊论文

  • 发表日期：

    2026年7月16日

查看全文
  • Nikhil Narayanan,
  • R. Kalaiselvi,
  • J. E. Judith

摘要

Aspect-based sentiment analysis (ABSA) facilitates fine-grained opinion analysis by integrating sentiment polarity with precise aspects in textual reviews. Despite tremendous improvements, contemporary ABSA methodologies continue to struggle with issues such as precise multi-word aspect extraction, sensitivity to noisy data, inadequate domain generalization and fragmented approach to aspect extraction and sentiment classification. In order to tackle the above-mentioned constraints, this investigation proposes a novel multilevel fast point graph transformer network (MFP-GTN) that incorporates aspect extraction and sentiment classification in to an individual framework. The design combines a joint neural conditional random field (CRF) for accurate multi-word aspect recognition with a multilevel graph-based rapid point transformer which records both long-range contextual dependencies and local sentiment cues. A dual-phase metaheuristic combining hybrid giant trevally white shark optimization (HGTWSO) and enhanced salp swarm optimization (ESSO) is implemented for optimum feature selection and model tuning, in order to lower redundancy, enhance convergence, and improve generalization. Detailed trails across multiple benchmark datasets, including Flipkart cell phone reviews, restaurant reviews, financial sentiment datasets and consumer car ratings, demonstrate the effectiveness of MFP-GTN model. The model attains accuracies of 99.36%, 99.99%, 99.91%, and 99.91%, respectively, consistently outperforming baselines. The outcomes exhibit significant robust cross-domain efficiency, interpretability and scalability, and, proving it optimal for practical, application-oriented ABSA tasks.