Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis (2020)

Abstract: In this paper, we explore a novel solution of constructing a heterogeneous graph for each instance by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect and propose an Interactive Graph Convolutional Networks (InterGCN) model for aspect sentiment analysis. Specifically, an ordinary dependency graph is first constructed for each sentence over the dependency tree. Then we refine the graph by considering the syntactical dependencies between contextual words and aspect-specific words to derive the aspect-focused graph. Subsequently, the aspect-focused graph and the corresponding embedding matrix are fed into the aspect-focused GCN to capture the key aspect and contextual words. Besides, to interactively extract the inter-aspect relations for the specific aspect, an inter-aspect GCN is adopted to model the representations learned by aspect-focused GCN based on the inter-aspect graph which is constructed by the relative dependencies between the aspect words and other aspects. Hence, the model can be aware of the significant contextual and aspect words when interactively learning the sentiment features for a specific aspect. Experimental results on four benchmark datasets illustrate that our proposed model outperforms state-of-the-art methods and substantially boosts the performance in comparison with BERT.

## introduction aspect sentiment analysis is a fine-grained sentiment analysis task, which aims to identify the sentiment polarity (e.g. positive, negative, or neutral) towards a given aspect (term) in a sentence. for example, given the aspects: food and service, and a sentence of review: great food but the service is dreadful, the sentiment polarity of aspect food is positive, while for the aspect service is negative. that is, in the task of aspect sentiment analysis, we need to discriminate sentiment polarities according to different aspects. the main challenge is that some aspects may contain no explicit sentiment expression. to vividly illustrate the challenge, we give examples shown in figure 1 , where the key contextual words and corresponding aspects are highlighted in the instances paired with their polarity labels. in figure 1 (a), there is a multiple words aspect (i.e. "soup for the udon"). we could readily resolve that the aspect word "soup" is the key aspect word of this aspect. thus, syntactical dependencies between this aspect word and the contextual words need to be attended for predicting aspect-specific sentiment. in figure 1 @xcite , aspect "toppings" and "place" are mentioned simultaneously in the sentence. there is a sufficiently clear positive sentiment word ("great") for aspect "toppings", while for aspect "place", which contains no sentiment expression, the sentiment polarity can also be identified thanks to the inter-aspect relations between aspect "toppings" and "place". hence, both aspect-focused and inter-aspect contextual relations should be considered for improving the performance of aspect sentiment analysis. recently, with the development of deep learning technique, many neural network-based methods achieve promising performance in aspect sentiment analysis @xcite @xcite the soup for the udon was soy sauce and water positive negative (a) example of aspect-focused relations great toppings definitely a place you need to check out (b) example of inter-aspect relations figure 1 : examples of the contextual relations of different aspect words within an aspect and the sentiment relations of different aspects in a sentence. @xcite @xcite . subsequently, attention-based neural models are widely used in this task, which can enforce the model to focus on the given aspect @xcite @xcite . in most previous methods, however, they generally embed aspect information into the sentence representation to learn the pertinent sentiment features for the specific aspect, which leads to a lack of capturing the inter-aspect sentiment relations for a specific aspect. analogously, most existing graph network-based model merely consider the syntactical dependencies between the specific aspect and the context @xcite @xcite , which is insufficient to focus on which contextual dependencies along with aspect-specific words are essential for the specific aspect and also largely ignore the sentiment relations between different aspects in the sentence. since intuitively, the role of distinct aspect word is different in deriving aspect expression. besides, there are intricate sentiment relations among different aspects in many instances. in this paper, we explore a novel solution to construct heterogeneous graphs of sentences via enriching the contextual syntactical dependency representations of the key aspect words and leveraging the mutual sentiment relations between different aspects in the context. based on it, an interactive graph convolutional networks (intergcn) model is proposed to leverage the sentiment dependencies of the context. here, the syntactical information from neighbors of each node is aggregated to derive the graph embeddings, so as to extract both aspect-focused and inter-aspect sentiment information for predicting aspect-specific sentiment polarity. the main contributions of our work can be summarized as follows: ## related work some early works mostly use machine learning algorithms to capture the sentiment polarity based on rich features about content and syntactic structures in aspect sentiment analysis @xcite @xcite . recently, deep learning models have achieved promising performance in aspect sentiment analysis @xcite @xcite @xcite @xcite . the majority of current approaches attempt to pay more attention to the specific aspect based on attention mechanism. @xcite exploited attention mechanism to capture the contextual representations via paying attention to the key parts of the sentence according to the given aspect. @xcite proposed an attention-based memory network to store contextual words and conducted multi-hop attention to derive the sentiment representation for the aspect. @xcite utilized a weighted-memory mechanism to produce a tailor-made memory for different opinion aspects based on memory network. in addition, @xcite utilized a gated cnn to selectively model the sentiment features according to the given aspect. @xcite adopted a capsule network to construct vector-based feature representation and cluster features by an em routing algorithm. @xcite considered the neighboring aspect-related information for the aspect-specific sentiment analysis with memory networks. graph convolutional network (gcn) has achieved promising performance in many nlp tasks @xcite @xcite . in aspect sentiment analysis, @xcite exploited gcn to capture syntactical information and word dependencies for the specific aspect over the dependency tree of a sentence. @xcite proposed a gcn model over the dependency tree of the sentence to enhance the feature representations of aspects learned by a bi-directional lstm (bi-lstm). in addition, to develop the merit of bert @xcite , a gcn model based on selective attention was proposed to extract and aggregate the most important contextual features for the aspect representation @xcite . the above gcn-based models, however, neither considered the specific aspect when constructing the graph of the sentence nor extracted inter-aspect sentiment relations for the specific aspect. to this end, based on the merit of gcn in aspect sentiment analysis, we explore a novel solution of constructing syntactical dependency graph for a sentence according to the specific aspect and propose an interactive graph convolutional networks (intergcn) model to extract both aspect-focused and inter-aspect sentiment features for the specific aspect. ## proposed approach as demonstrated in figure 2 , the architecture of the proposed intergcn model mainly contains two components: 1) aspect-focused graph convolutional networks, which aims to extract the aspect-specific sentiment features based on our novel syntactical dependency graph of the sentence, and 2) inter-aspect graph convolutional networks, which is designed to derive the sentiment relations between different aspects. the feature representations captured from these two components are interactively combined to produce the sentiment features for the specific aspect. we first assume that there is a sentence with n words and two aspects, i.e. ## conclusion in this paper, we explore a novel solution of constructing aspect-focused and inter-aspect dependency graphs for aspect sentiment analysis. based on it, an interactive graph convolutional networks (in-tergcn) model is proposed to extract the aspect-specific sentiment features from the aspect-focused and inter-aspect perspective. to this end, the proposed intergcn model can pay significant attention to the key aspect words when dealing with multiple words aspect, and connect the valuable sentiment features of related aspects when considering an aspect without distinct sentiment expression. experimental results on four benchmark datasets show that the proposed intergcn can outperform state-of-the-art methods including remarkable gcn-based models and bert-based models.