SSEGCN : Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis (2022)

Abstract: Aspect-based Sentiment Analysis (ABSA) aims to predict the sentiment polarity towards a particular aspect in a sentence. Recently, graph neural networks based on dependency tree convey rich structural information which is proven to be utility for ABSA. However, how to effectively harness the semantic and syntactic structure information from the dependency tree remains a challenging research question. In this paper, we propose a novel Syntactic and Semantic Enhanced Graph Convolutional Network (SSEGCN) model for ABSA task. Specifically, we propose an aspect-aware attention mechanism combined with self-attention to obtain attention score matrices of a sentence, which can not only learn the aspect-related semantic correlations, but also learn the global semantics of the sentence. In order to obtain comprehensive syntactic structure information, we construct syntactic mask matrices of the sentence according to the different syntactic distances between words. Furthermore, to combine syntactic structure and semantic information, we equip the attention score matrices by syntactic mask matrices. Finally, we enhance the node representations with graph convolutional network over attention score matrices for ABSA. Experimental results on benchmark datasets illustrate that our proposed model outperforms state-of-the-art methods.

## introduction aspect-based sentiment analysis (absa) aims to determine the sentiment polarity of a given aspect term in a sentence, where sentiment polarity includes positive, negative and neutral. for example, in figure 1 , absa determines the sentiment towards the aspects "food" and "service". for aspect term "food", the sentiment polarity is negative, but "service" is positive. that is, we need to discriminate sentiment polarities according to * corresponding author. different aspects. the main idea of most works is to model the dependency relation between aspects and their associated opinion words. prior studies exploit attention mechanism @xcite @xcite @xcite @xcite to model the correlations between aspect term and the context. however, attention mechanism is vulnerable to noise in sentences, i.e., the irrelevant words. recent works on absa leverage graph neural networks (gnns) over the dependency tree of a sentence to exploit syntactic structure @xcite @xcite @xcite @xcite . in these works, @xcite employ graph convolutional network (gcn) to integrate the syntactic information. @xcite propose a gcn model to enhance the feature representations of aspects learned by a bi-directional long short term memory (bi-lstm). however, these two studies treat all the neighbor nodes of the current node in the graph equally, and are limited in lacking of efficient mechanisms to distinguish the importance of neighbor nodes. accordingly, noisy nodes may cause the model to misjudge the sentiment polarity. to tackle this problem, @xcite design a target-dependent graph attention network that updates each node representation by utilizing multi-head attention. the attention mechanism is used to consider semantic correlation of each neighbor node, but it is a lo-cal attention by only calculating the importance of neighboring nodes and neglects the global information of the sentence. following this line, @xcite and @xcite combine a syntactic structure graph with a latent semantic graph but the two graphs are constructed independently. notablely, existing gcn-based approaches do not fully leverage syntactic structure, where only the information of the neighbor nodes is considered. moreover, some hard cases obscurely express sentiment of aspect term, there is no direct syntactic relationship between aspect term and opinion words. how to capture the information of secondorder nodes, third-order nodes and even the global syntactic structure in one shot is still a challenge. recently, most methods apply multi-layer gcns to derive the expression of opinion words which brings potential noise. for instance, consider the dependency tree as depicted in figure 1 , a dependency connection exists between the aspect "food" and the opinion word "good". however, the "good" refers to another aspect "service". for aspect term "food", the representation of word "not" is obtained by utilizing two-layer gcn. at the same time, noisy word "not" is also obtained for aspect term "service". as a result, simply considering the syntactic structure of dependency tree might be unsatisfactory. it is necessary to harness the aspect-related semantic information for different aspect terms. in this paper, we propose a novel syntactic and semantic enhanced graph convolutional network (ssegcn) model for integrating the syntactic and semantic information of a sentence to solve the above issues. firstly, ssegcn captures the contextualized word representations with sentence encoder. secondly, to model particular semantic correlations for different aspect terms, we propose an aspect-aware attention mechanism to combine with self-attention. the aspect-aware attention learns aspect-related semantic information, while selfattention learns global semantic of the sentence. we take the obtained attention scores as the initial adjacency matrices for gcn. besides, to fully utilize syntactic structure to complement semantic information, rather than just syntactic first-order neighbor node information, we construct the syntactic mask matrices calculated by the different distances between words in the syntactic dependency structure of the sentence to learn structure information from local to global. then, we combine adjacency matrices with syntactic mask matrices to enhance the conventional gcn. finally, a multilayer graph convolution operation is implemented to obtain aspect-specific features for aspect term sentiment classification. we evaluate our approach on three benchmark datasets. the results show that our model is more effective than a range of baselines and achieves new state-of-the-art performance. our contributions are summarized as follows: ## related work aspect-based sentiment analysis is a fine-grained sentiment analysis task and generally treated as a classification problem. earlier methods @xcite ) manually defined some syntactic rules to predict the sentiment polarity of aspect term. most recent researches solve aspect-based sentiment analysis by utilizing attention-based neural network to model semantic correlation between context and aspect term @xcite @xcite @xcite @xcite . among them, @xcite utilized attention mechanism to concentrate on different parts of a sentence to generate an attention vector for aspect sentiment classification. @xcite proposed a multi-layer attention network to infer the sentiment polarity for the aspect. @xcite introduced an interactive attention mechanism to generate the representations for aspects and contexts separately. @xcite designed a hierarchical aspect-specific attention model for aspect sentiment classification. @xcite employed a constrained attention network with both orthogonal regularization and sparse regularization. another trend explicitly leverages dependency tree. syntactical information can establish relation connections between aspect and corresponding opinion words, gcn based on dependency tree have achieved impressive performance in absa. @xcite stacked a gcn layer to extract rich representations over dependency tree. @xcite build aspectfocused and inter-aspect graphs to learn aspectspecific sentiment features. @xcite constructed a global lexical graph to capture the word co-occurrence relation and combined a global lexical graph and a syntactic graph. @xcite constructed a sentiment enhancement graph by integrating the sentiment knowledge from sen-ticnet to consider the affective information between opinion words and aspect term. @xcite utilized dependency types and distinguished different relations in the dependency tree. however, these approaches generally ignore the effective fusion of syntactic structure and semantic correlation to obtain richer information. ## proposed ssegcn figure 2 gives an overview of ssegcn. in this section, we describe the ssegcn model which is mainly composed of three components: the input and encoding layer, the attention layer, the syntax-mask layer and the gcn layer. next, components of ssegcn will be introduced separately in the rest of the sections. ## conclusion in this paper, we propose a ssegcn architecture which integrates semantic information along with the syntactic structure for absa task. specifically, we first design an aspect-aware attention mechanism, which is responsible for learning aspectrelated semantic information. then, we combine it with the self-attention to compose the attention layer. furthermore, we construct syntactic mask matrices of a sentence according to the different syntactic distances that learn local to global structure information. consequently, we combine attention score matrices with syntactic mask matrices to fuse the semantic and syntactic information. experimental results demonstrate the effectiveness of our approach on three public datasets.