Abstract: With the continuous advancement of social media and online service platforms, user reviews have increasingly become a critical source of information influencing consumer decisions and product evaluations. Aspect-based sentiment analysis (ABSA), as a key research direction in fine-grained sentiment computing, still faces significant challenges in practical applications—particularly prominent semantic ambiguity in textual content and insufficient extraction of sentiment cues. To address these limitations, this paper proposes CKMA, a novel aspect-based sentiment analysis model that integrates knowledge graph embeddings with a multi-channel attention mechanism.The CKMA model first leverages knowledge graph embedding techniques to map entities and their relationships from external knowledge bases into low-dimensional semantic vectors, which are then fused with textual representations to alleviate semantic ambiguity commonly observed in user reviews. Building upon this knowledge-enhanced representation, we design a parallel multi-channel feature extraction framework comprising three distinct channels: a structured information channel, a context-aware channel, and an aspect-focused channel. Through a staged fusion strategy, this framework enables collaborative modeling of diverse semantic and syntactic signals, thereby enhancing the model’s capacity to capture aspect-relevant sentiment features. To mitigate the loss of original semantic information during deep feature learning, we further introduce a joint fusion mechanism that combines the knowledge-enhanced word-level representations with the outputs of the multi-channel attention modules, thereby improving the completeness and robustness of the final feature representation.Extensive experiments conducted on four widely used benchmark datasets—Restaurant14, Restaurant16, Laptop14, and Twitter—demonstrate that the proposed method achieves superior performance in terms of both accuracy and Macro-F1 scores. Notably, CKMA exhibits more pronounced advantages on datasets characterized by complex syntactic structures, validating the effectiveness of our synergistic modeling strategy that jointly exploits structural and semantic information for aspect-based sentiment analysis.

摘要： 随着社交媒体与在线服务平台的持续发展，用户评论逐渐成为影响消费决策与产品评价的重要信息来源。方面级情感分析作为细粒度情感计算的重要研究方向，在实际应用中仍面临文本语义歧义突出以及情感线索提取不充分等问题。针对上述不足，本文提出一种融合知识图谱嵌入与多通道注意力机制的方面级情感分析模型CKMA。该模型首先引入知识图谱嵌入方法，将外部知识中实体及其关系映射为低维语义向量，并与文本表示相结合，以缓解评论文本中常见的语义歧义现象。在此基础上，构建由结构化信息通道、上下文相关通道和方面专注通道组成的并行多通道特征提取框架，通过分阶段融合策略实现不同语义与句法信息的协同建模，从而提升模型对方面相关情感特征的刻画能力。为避免深层特征学习过程中原始语义信息的损失，进一步将知识增强的词级表示与多通道注意力输出进行联合融合，以增强特征表达的完整性与稳健性。 在Restaurant14、Restaurant16、Laptop14和Twitter四个公开数据集上的实验结果表明，所提出方法在准确率和Macro-F1指标上均取得了较优表现，尤其在句法结构较为复杂的数据集上展现出更明显的性能优势，验证了所提出结构与语义协同建模策略在方面级情感分析任务中的有效性。