Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pages 6319–6329 August 1–6, 2021. ©2021 Association for Computational Linguistics 6319 Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis Ruifan Li1∗, Hao Chen1, Fangxiang Feng1, Zhanyu Ma1, Xiaojie WANG1, and Eduard Hovy2 1 School of Artificial Intelligence, Beijing University of Posts and Telecommunications, China 2 Language Technologies Institute, Carnegie Mellon University, USA {rfli, ccchenhao997, fxfeng, mazhanyu, xjwang}@bupt.edu.cn hovy@cmu.edu Abstract Aspect-based sentiment analysis is a fine- grained sentiment classification task. Re- cently, graph neural networks over depen- dency trees have been explored to explicitly model connections between aspects and opin- ion words. However, the improvement is lim- ited due to the inaccuracy of the dependency parsing results and the informal expressions and complexity of online reviews. To over- come these challenges, in this paper, we pro- pose a dual graph convolutional networks (Du- alGCN) model that considers the complemen- tarity of syntax structures and semantic cor- relations simultaneously. Particularly, to al- leviate dependency parsing errors, we design a SynGCN module with rich syntactic knowl- edge. To capture semantic correlations, we design a SemGCN module with self-attention mechanism. Furthermore, we propose or- thogonal and differential regularizers to cap- ture semantic correlations between words pre- cisely by constraining attention scores in the SemGCN module. The orthogonal regular- izer encourages the SemGCN to learn seman- tically correlated words with less overlap for each word. The differential regularizer encour- ages the SemGCN to learn semantic features that the SynGCN fails to capture. Experimen- tal results on three public datasets show that our DualGCN model outperforms state-of-the- art methods and verify the effectiveness of our model. 1 Introduction Sentiment analysis has become a popular topic in natural language processing (Liu, 2012; Li and Hovy, 2017). Aspect-based sentiment analysis (ABSA) talks an entity-level oriented fine-grained sentiment analysis task that aims to determine sen- timent polarities of given aspects in a sentence. In ∗Corresponding author. Figure 1: An example sentence with its dependency tree from the restaurant reviews. This sentence contains two aspects but with opposite sentiment polarities. Figure 1, the comment is about a restaurant review. The sentiment polarity of the two aspects “price” and “service” are positive and negative, respec- tively. Thus, ABSA can precisely identify user’s attitudes towards a certain aspect, rather than sim- ply assigning a sentiment polarity for a sentence. The key point in solving the ABSA task is to model the dependency relationship between an as- pect and its corresponding opinion expressions. Nevertheless, there probably exist multiple aspects and different opinion expressions in a sentence. To judge the sentiment of a particular aspect, previous studies (Wang et al., 2016; Tang et al., 2016a; Ma et al., 2017; Chen et al., 2017; Fan et al., 2018; Huang et al., 2018; Gu et al., 2018) have proposed various recurrent neural networks (RNNs) with at- tention mechanisms to generate aspect-specific sen- tence representations and have achieved appealing results. However, an inherent defect makes …

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