Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network

Proceedings of the 28th International Conference on Computational Linguistics, pages 833–843 Barcelona, Spain (Online), December 8-13, 2020 833 Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network Hongjie Cai1, Yaofeng Tu2, Xiangsheng Zhou2, Jianfei Yu1, Rui Xia1∗ 1School of Computer Science and Engineering, Nanjing University of Science and Technology 2ZTE Corporation, Shenzhen, China {hjcai, jfyu, rxia}@njust.edu.cn {zhou.xiangsheng, tu.yaofeng}@zte.com.cn Abstract Most of the aspect based sentiment analysis research aims at identifying the sentiment polarities toward some explicit aspect terms while ignores implicit aspects in text. To capture both explicit and implicit aspects, we focus on aspect-category based sentiment analysis, which involves joint aspect category detection and category-oriented sentiment classification. However, currently only a few simple studies have focused on this problem. The shortcomings in the way they defined the task make their approaches difficult to effectively learn the inner-relations between categories and the inter-relations between categories and sentiments. In this work, we re-formalize the task as a category-sentiment hierarchy prediction problem, which contains a hierarchy output structure to first identify multiple aspect categories in a review sentence, and jointly predict the sentiment for each of the identified categories. Specifically, we propose a Hierarchical Graph Convolutional Network (Hier-GCN), where a lower-level GCN is to model the inner-relations among multiple categories, and the higher-level GCN is to capture the inter-relations between aspect categories and sentiments. Extensive evaluations demonstrate that our hierarchy output structure is superior over existing ones, and the Hier-GCN model consistently achieves the best results on four benchmarks. 1 Introduction As an important fine-grained subtask in the field of sentiment analysis, Aspect-Based Sentiment Classi- fication (ABSC) aims to detect the sentiment polarities of aspect terms mentioned in a review sentence. For example, in Table 1, given the aspect term food, it is expected to identify its corresponding sentiment polarity as positive. The main limitation of ABSC lies in that aspect terms need to be annotated before aspect sentiment classification, which is not applicable to real applications. To address this problem, many studies have been proposed to explore Aspect Term-based Sentiment Analysis (ATSA), which per- forms aspect term extraction and aspect sentiment classification jointly (Mitchell et al., 2013; Zhang et al., 2015; Li et al., 2019; Luo et al., 2019; Hu et al., 2019). However, ATSA still suffers from a major obstacle that it only considers explicit aspects but completely ignore implicit aspects in text. Take the review in Table 1 as an example. Although the second clause does not mention any aspect term, it clearly expresses user’s negative sentiment towards the service. More importantly, we observe that existing benchmark datasets contain a large amount of such reviews. For instance, Table 2 shows the proportion of reviews with implicit aspects in the Restaurant dataset from SemEval 2015 and 2016, and it is clear that nearly 25% of the examples contain implicit aspects. Since these examples also convey valuable information, we should no longer ignore them, as the previous ATSA methods do. Motivated by this, we focus on Aspect-Category based Sentiment Analysis (ACSA) …

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