Abstract: The fine-grained sentiment analysis of user comments for specific aspects is a popular research topic in the field of natural language processing. For the flexibility of comment statements in content expression and syntactic structure, the integrated use of lexical, syntactic and semantic knowledge to enhance the feature representation of comment statements is a major research idea at present. Based on this, a graph convolutional network model for multi-view fusion representation is proposed in this paper. First, the model learns to obtain context-based enhanced representations of comment statements through self-attention and aspect-specific attention. Second, two different representations of comment utterances based on syntax and semantics are obtained through graph convolution operations using syntactic dependency information and word co-occurrence information, respectively. Finally, a hierarchical fusion approach is designed based on obtaining three different view representations to achieve information sharing and complementarity among different view representations by combining and convolving the three representations. Experimental results on five publicly available datasets show that the model achieves better performance than existing models.

Key words: aspect-level sentiment analysis, graph convolution network, attention mechanism, multiview representation

摘要： 面向特定方面的用户评论细粒度情感分析是当前自然语言处理领域一个热门的研究话题，针对评论语句在内容表达和句法结构上的灵活性，综合运用词性、句法、语义等知识增强评论语句的特征表示是当前一种主要的研究思路。基于此，提出一种多视图融合表示的图卷积网络模型。该模型通过自注意力和特定方面注意力，学习得到评论语句基于上下文的增强表示；分别利用句法依赖信息和词共现信息，通过图卷积操作得到评论语句基于句法和基于语义的两种不同表示；在获得三种不同视图表示的基础上设计了一种分层融合方式，通过对三种表示的不同组合与卷积操作实现不同视图表示间的信息共享与互补。五个公开数据集上的实验结果表明该模型较现有模型取得了更好的性能。

关键词: 方面级情感分析, 图卷积网络, 注意力机制, 多视图表示