计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 230-237.doi: 10.11896/jsjkx.220300008
阳影1, 张凡1,2, 李天瑞1,2,3
YANG Ying1, ZHANG Fan1,2, LI Tianrui1,2,3
摘要： 方面级情感分析是一项细粒度情感分析任务,其目标是对句子中给定的方面词进行情感极性分类。当前的情感分类模型大多在依存句法树上构建图神经网络,从依存句法树上学习方面词与上下文之间的信息,缺乏对句子中情感知识的挖掘。针对这个问题,文中提出了一种基于情感知识的双通道图卷积网络的情感分类模型(Dual-channel Graph Convolutional Network with Sentiment Knowledge,SKDGCN)。该模型由情感增强的依存图卷积网络(Sentiment-enhanced Dependency Graph Convolutional Network,SDGCN)和注意力图卷积网络(Attention Graph Convolutional Network,AGCN)组成,两个图卷积网络分别学习方面词与上下文词的句法依赖关系和语义关系。具体地,SDGCN在句法依存树上融合SenticNet中的情感知识以增强句子的依赖关系,使得模型既考虑了上下文词与方面词的句法关系,也考虑了上下文中意见词与方面词的情感信息;AGCN使用注意力机制学习方面词与句子中上下文的语义相关性;最后使两个图卷积网络交互学习各自的信息进行情感分类。实验结果表明,该模型在多个公开数据集上表现优异,并通过消融实验验证了各个模块的有效性。
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