Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network (2020)

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 piece of text, and then 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 can consistently achieve the best results on four benchmarks.

## introduction as an important fine-grained subtask in the field of sentiment analysis, aspect-based sentiment classification (absc) aims to detect the sentiment polarities of aspect terms mentioned in a review sentence. for example, in 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 motivated by this, we focus on aspect-category based sentiment analysis (acsa) in this paper, aiming to perform joint aspect category detection and category-oriented sentiment classification. compared with atsa, acsa has the following two advantages: on the one hand, for each aspect mentioned in a review: the food here is rather good, but only if you like to wait for it. in this paper, we re-formalize the task as a category-sentiment hierarchy prediction problem, which contains a two-layer hierarchy output structure. the lower layer is to detect aspect categories, which can be modelled as a multi-label classification problem (i.e., one review may contain more than one category). the higher layer is to perform category-oriented sentiment classification, which can be modelled as a multi-class classification problem for each detected category. under the hierarchy output structure, our model contains three modules: the bottom module leverages bert to obtain hidden representations of the two sub-tasks respectively. in the middle module, we propose a hierarchical graph convolutional network (hier-gcn), where the lower-level gcn is to model the inner-relations among multiple categories, and the higher-level gcn is to capture the interrelations between categories and category-oriented sentiments. based on the interactive representations generated from hier-gcn, the top module performs category-sentiment hierarchy prediction to generate the final output. we conduct experiments on four benchmark product review datasets from semeval 2015 and 2016. the results prove that the hierarchy output structure achieves better performance than other existing structures. on this basis, the proposed hier-gcn architecture can bring additional performance gains, and consistently achieves the best results across the four datasets. further analysis also proves the effectiveness of hier-gcn in cases of both explicit aspect and implicit aspect. ## problem formalization given a review sentence with n words @xmath0, . . . , w n ], aspect category-based sentiment analysis (acsa) aims to detect all the mentioned aspect categories, and identify the sentiment for each detected category. formally, let @xmath1, . . . , c m } be a set of m predefined aspect categories, and @xmath2, negative, neutral} be the label set of sentiment polarities. accordingly, for each input r, the goal of acsa is to generate a set of category-sentiment pairs, denoted as {. . . , (ŷ c i , ŷs i ), . . .}, where ŷc i is the i-th aspect category mentioned in r, ŷs i is its corresponding sentiment. as shown in fig. 1 (a), one possible solution to this task is to consider all the combinations of categorysentiment pairs, denoted by cartesian product. specifically, for each aspect category c i , we can model its sentiment prediction as a multi-label classification task to generate the output ŷs @xmath3, 1, 2}, which indicates negative, neutral, and positive sentiment polarity, respectively. note that if ŷc @xmath4, it indicates the absence of the category c i and its corresponding sentiment polarity. since cartesian product suffers from the risk of generating multiple sentiments for each category, an alternative solution is to add one label to the sentiment label space to predict the presence or absence of each category @xcite . in this case, the occurrence and the sentiment prediction of each category is unified as a simple multi-class classification problem. however, both approaches simply unify aspect category detection and category-oriented sentiment classification as one task, while ignoring the internal relationship between the two sub-tasks. intuitively, if an aspect category is not detected, it is unnecessary to perform sentiment classification for it, and if multiple categories are detected, the relations among their sentiment outputs should be fully utilized. motivated by this, we re-formalize the acsa task as a category-sentiment hierarchy prediction problem: ## the proposed approach the overall architecture of the proposed framework is illustrated in fig. 2 . we use bert as the basic encoder of our model to encode the contextual information of a sentence and generate context-aware category representations and sentiment representations, respectively (section 3.1); next, we use two hierarchical graph convolution networks to capture the inner-relation between multiple categories and the inter-relation between categories and sentiment polarities, respectively (section 3.2); finally, a hierarchical output and integration module is designed to construct the final prediction from category prediction and sentiment prediction (section 3.3). ## conclusion in this paper, we first examined the limitations of existing approaches for the task of aspect categorybased sentiment analysis, and proposed a hierarchy output structure to first detect the aspect categories, and then perform sentiment classification for each detected category. given the output structure, we further proposed a hierarchical graph convolutional network to capture the inner-relations between multiple categories as well as the inter-relations between categories and sentiments. experimental results on four benchmark datasets show that our hierarchy output structure performs significantly better than any existing output structure, and hier-gcn can outperform a number of highly competive approaches including a strong hierarchical transformer model proposed by us.