计算机与现代化Issue(2)：53-60,8.DOI:10.3969/j.issn.1006-2475.2026.02.007
基于语义增强的多特征融合方面级情感分析
Semantic-enhanced Multi-feature Fusion for Aspect-level Sentiment Analysis
摘要
Abstract
Currently,most sentiment analysis models often rely on semantic structure of syntactic dependency trees to extract se-mantic information.However,there exists a gap between syntactic dependency structures and semantic sentiment analysis tasks.To address this issue,this paper proposes a semantic-enhanced multi-feature fusion method for aspect-level sentiment analysis.This method introduces the Abstract Meaning Representation(AMR)structure and combines the extraction methods of global and local features for aspect-level sentiment analysis tasks.Firstly,the relation embedding representation extracted from AMR and the sentence embedding representation extracted from BERT are fused to obtain the semantic information of the input text.Secondly,Bi-LSTM and capsule networks are employed to extract deep global and local features.Finally,a multi-head self-attention mechanism is applied to integrate multi-dimensional features,effectively capturing the associations between aspect terms and contextual sentences.Experiments on multiple public datasets demonstrate the effectiveness of the proposed method.On the Restaurant dataset,the model achieves an accuracy of 87.77% and a recall of 82.60%,while on the Twitter dataset,it at-tains an accuracy of 78.71% and a recall of 77.54%.The experimental results indicate that the proposed method significantly im-proves the performance of aspect-level sentiment analysis.关键词
方面级情感分析/抽象语义表示/胶囊网络/多头注意力机制/特征融合Key words
aspect-level sentiment analysis/abstract meaning representation/capsule network/multi-head attention mecha-nism/feature fusion分类
信息技术与安全科学引用本文复制引用
王浩畅,崔思敏,赵铁军,贾先珅..基于语义增强的多特征融合方面级情感分析[J].计算机与现代化,2026,(2):53-60,8.基金项目
国家自然科学基金资助项目(61402099,61702093) （61402099,61702093）
