Abstract: Aspect-based sentiment analysis (ABSA) has become one of the hottest research issues in the field of natural language processing. Compared with traditional sentiment analysis technology, aspect-based sentiment analy-sis can judge the sentiment tendency of multiple targets in a sentence, and more accurately mine the sentiment polarity of the aspect. Currently, the models combining attention mechanism and neural network only consider the impact of aspects on the context, and often ignore the context information of sentences and background knowledge. To solve the above problems, an interactive attention neural network model based on knowledge maps and graph convolution network is proposed to inject background information and language knowledge into review text. Firstly, the polysemy problem of vocabulary under different contexts is addressed via knowledge maps. Secondly, text graph convolution networks are used to improve the syntactic structure information of review text. Finally, the context and aspect of the review text are coordinated and optimized through an interactive attention mechanism. Experimental results on five public datasets show that rational use of external knowledge is an effective strategy for enhancing the performance of aspect-based sentiment analysis model.

Key words: knowledge graph, lexical and syntactic relations, graph neural networks, aspect-based sentiment analysis, interactive attention mechanism

摘要： 方面级情感分析（ABSA）已经成为自然语言处理领域的研究热点，与传统的情感分析技术相比，基于方面的情感分析能够判断句子中多个方面的情感倾向，可以更加准确地挖掘用户对方面的情感极性。当前，将注意力机制和神经网络相结合的模型在解决方面级情感分析任务时大多仅考虑方面对上下文的影响，且时常忽略句子中的相关语法信息和背景知识。针对上述问题，提出一种借助知识图谱和图卷积网络的交互注意力神经网络模型，为评论文本注入背景信息和语言知识。首先，利用知识图谱解决词汇在不同语境下的一词多义性问题。其次，利用文本图卷积网络完善评论语句的语法结构信息。最后，通过交互注意力机制实现评论文本上下文与评价方面的协调优化。最终在五个公开数据集上的实验结果表明，合理利用外部知识是改善方面级情感分析模型性能的有效策略。

关键词: 知识图谱, 词汇句法关系, 图神经网络, 方面级情感分析, 交互注意力机制