关键词: 方面级情感分析, 异构图, 双通道注意力机制, 图卷积网络, 义原知识

Abstract: Aspect-based sentiment analysis is an important task in natural language processing. Existing methods generally adopt single-type nodes to model semantic associations between aspects and opinion words, failing to effectively distinguish the contributions of different node types to sentiment representation. Moreover, current models usually construct syntactic trees using dependency parsers for semantic analysis, ignoring fine-grained semantic features and implicit sentiment modification relations contained within words. To address these issues, a heterogeneous multi-source graph convolutional network (HMS-GCN) is proposed. First, a heterogeneous graph with differentiated constraints is constructed to capture semantic and sentiment information from multi-type nodes and their corresponding edges, utilizing a dual-channel attention mechanism to weigh node types and individual node importance. Second, sememe knowledge enhances semantic representation of initial textual features, and phrase-level structural information is integrated into the syntactic adjacency matrix to improve syntactic dependency comprehension. Finally, heterogeneous graph representations and enhanced knowledge features are combined for sentiment prediction. Experimental results on public datasets (Twitter, Lap14, Rest15, Rest16) demonstrate that the proposed method outperforms the baseline model ISSK-GCN, with accuracy improvements of 1.18, 1.33, 1.55, 2.41, and macro-F1 improvements of 2.16, 2.40, 0.69, 5.96, validating its effectiveness.

Key words: Keywords: aspect-based sentiment analysis, heterogeneous graph, dual-channel attention mechanism, graph convolutional network, sememe knowledge