DAGCN : Distance-based and Aspect-oriented Graph Convolutional Network for Aspect-based Sentiment Analysis (2024)

Abstract: Aspect-based sentiment analysis (ABSA) is a task that aims to determine the sentiment polarity of aspects by identifying opinion words. Recent advancements have predominantly been rooted either in semantic or syntactic methods. However, both of them tend to interference from local factors such as irrelevant words and edges, hindering the precise identification of opinion words. In this paper, we present Distance-based and Aspect-oriented Graph Convolutional Network (DAGCN) to address the aforementioned issue. Firstly, we introduce the Distance-based Syntactic Weight (DSW). It focuses on the local scope of aspects in the pruned dependency trees, thereby reducing the candidate pool of opinion words. Additionally, we propose Aspect-Fusion Attention (AF) to further filter opinion words within the local context and consider cases where opinion words are distant from the aspect. With the combination of DSW and AF, we achieve precise identification of corresponding opinion words. Extensive experiments on three public datasets demonstrate that the proposed model outperforms state-of-the-art models and verify the effectiveness of the proposed architecture.

## introduction aspect-based sentiment analysis (absa) is a fine-grained sentiment analysis task that aims to determine the sentiment polarity of a given aspect within a sentence. the sentiment polarity can be classified into three categories: positive, neutral, and negative. for instance, in figure 1 , the aspect "skype" can be determined to have positive sentiment polarity based on opinion word "cool". in fact, opinion words carry certain sentiment information and absa primarily focuses on identifying opinion words that are relevant to the aspect. previous studies have explored heavily on attention mechanism methods and achieved promising results @xcite @xcite @xcite . in these works, attention mechanism is utilized to model the correlation between aspects and context words. however, they always suffer noise that high weights might be given wrongly to words that are irrelevant to the aspect. for the purpose of filtering out the noise brought by the attention mechanism, semantic-relative distance (srd) is proposed to measure semantic correlation degree @xcite . it could help attention mechanism identify opinion words more accurately in local range. therefore, a plenty of studies @xcite utilized srd or its variants to improve absa task. however, this local scope is still large, and it also tends to recognize other words as opinion words mistakenly. to prove it, we conduct a statistical analysis of srd with the datasets, lap14 and res14, provided by @xcite . on the other hand, more significant efforts @xcite @xcite @xcite @xcite in absa have focused on dependency tree, which has ability to analyze syntactic structure from the grammatical perspective. subsequently, gcns and gats aggregate node features over the adjacency matrix derived from the dependency tree to determine the sentiment polarity of the aspect. however, dependency tree just reveals whether syntactic connection exists between context words and aspects, and it's hard to distinguish which words are valuable. to tackle the issues above, we would like to reduce noise in dependency tree just like srd and focus on opinion words precisely. some studies @xcite @xcite have shown that in most cases opinion words are close to the aspects in a dependency tree, which means that we can only consider context words surrounding aspects syntactically. figure 2 demonstrates that syntactic distance (sd) between opinion words and aspects in most cases is shorter than 2 and the data with sd larger than 6 comprises an extremely small portion. besides, therefore, we propose distance-based syntactic weight (dsw) computed through aspect-oriented dependency tree @xcite . note that aodt is utilized from the perspective of sd in our paper, rather than dependency relationships. dsw characterizes the syntactic correlation strength between context words and aspects, and enhances the precision of identifying opinion words. then, we define distance-based weighted matrix (dwm) to store dsw. considering that opinion words are far away from aspects, we introduce aspect-fusion attention (af) to further discern candidate opinion words within both local and long range scopes. finally, we combine dwm and af to build an adjacency matrix and a graph convolutional network (aogcn) is constructed over it. due to the fact that gcns over dependency tree perform poorly on the reviews with informal expression, similar to dualgcn (li et al., 2021a), we build another gcn (sagcn) by employing self-attention mechanism. specifically, we incorporate a kullback-leibler (kl) divergence loss to ensure that the two gcns learn distinct features, with aogcn focusing on syntactic information and sagcn emphasizing semantic information. main contributions are summarized as follows: ## related work aspect-based sentiment analysis primarily focuses on utilizing opinion words to determine the sentiment polarity of aspects. early works @xcite often relied on constructing aspect-specific sentiment lexicons or manually specified features, without incorporating syntactic features. recently, lots of works have focused extensively on attention mechanism to determine the semantic correlation between context words and aspects @xcite @xcite @xcite @xcite . @xcite designed stacked attention mechanisms to capture both local and global features, enhancing the performance of lstm. @xcite proposed a novel sparse self-attention mechanism to differentiate the importance of different words for sentiment polarity. there have been several studies focusing on the distance between aspects and opinion words, as it is believed to contain rich semantic knowledge @xcite . in addition to utilizing srd to extract semantic information, @xcite also used the absolute distance between aspects and context words to differentiate their importance. @xcite created a local context weighted adjacency graph with srd in order to emphasize significance of local context and avoid long range influence. however, these methods were hard to precisely identify opinion words within relatively large local scopes. in addition, the dependency tree has been widely used before. nguyen and shirai (2015) integrated syntactic information by combining dependency relation and phrases. @xcite utilized underlying syntactic information to learn a highlevel feature representation. with the emergence of graph convolutional networks (gcns) and graph attention networks (gats), gcn-based and gatbased methods have been employed to learn syntactic information from the dependency tree @xcite @xcite @xcite @xcite @xcite @xcite . @xcite selected relevant knowledge from a knowledge graph and incorporated it into the dependency tree to improve its expressive power. @xcite considered the relationship labels of the dependency tree and proposed an adaptive fusion module for semantic information. unfortunately, the dependency tree contained considerable noise, with non-opinion words interfering with the model's judgment. additionally, these methods failed to recognize the contribution of opinion words to the absa task. ## the proposed model figure 3 illustrates the overview of dagcn. given a pair of sentence-aspect (s, a), where @xmath2, w 2 , ..., w n } and @xmath3, a 2 , ..., a m }, an aspect is a part of the sentence. m is the end position of aspect in s. before feeding the input into the model, we first map each word to its embedding with the embedding table @xmath4, where |v| represents the size of the embedding table and d e denotes the dimension of the word embeddings. then, an encoder such as bilstm or bert is utilized to learn contextual information from the sentence. the input x is fed into the encoder, resulting in hidden state vectors @xmath5, h 2 , ..., h n }, where h @xmath6. we use h as the initial node representation and input it into aogcn and sagcn for aggregation operations. for the bert encoder, we construct inputs in the format required by bert, which is "[cls] sentence [sep] aspect [sep]". [cls] and [sep] are special tokens in bert used for classification and sentence separation, respectively. subsequently, we elaborate on the details of dagcn. ## experiments statistics for the three experimental datasets and implementation details could be found in a.1 and a.2, respectively. ## conclusion in this paper, we have presented a novel dagcn model. with the statistical analysis of syntactic distance, there's a higher probability of opinion words appearing in the local context. hence, we employ dsw to assign higher weights to words closer to the aspects in terms of syntactic distance, and it could also eliminate noise in the dependency tree. furthermore, we construct dwm to store dsw and update dwm by af to enhance the accuracy of identifying opinion words and accommodate scenarios where opinion words are distant from the aspects. finally, inspired by previous work, we define a sagcn to deal with some reviews with unstructured syntax and kl divergence is integrated into the loss function to guarantee distinct learning for aogcn and sagcn. compared with other baselines, dagcn achieves superior performance on public datasets, which demonstrates the effectiveness of the proposed architecture.