Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis (2023)

Abstract: Aspect-based sentiment analysis (ABSA) aims to align aspects and corresponding sentiment expressions, so as to identify the sentiment polarities of specific aspects. Most existing ABSA methods focus on mining syntactic or semantic information, which still suffers from noisy interference introduced by the attention mechanism and dependency tree when multiple aspects exist in a sentence. To address these issues, in this paper, we revisit ABSA from a novel perspective by proposing a novel scope-assisted multi-view graph contrastive learning framework. It not only mitigates noisy interference for better locating aspect and its corresponding sentiment opinion with aspect-specific scope, but also captures the correlation and difference between sentiment polarities and syntactic/semantic information. Extensive experiments on five benchmark datasets show that our proposed approach substantially outperforms state-of-the-art methods and verifies the effectiveness and robustness of our model.

## introduction aspect-based sentiment analysis (absa) is a finegrained sentiment classification task, aiming at identifying the sentiment polarities (i.e., positive, neutral, or negative) of specific aspects in a given sentence @xcite @xcite . different from document-or sentencelevel sentiment analysis, different aspects in a sentence may express different sentiment polarities. for example, given a sentence "service was slow but people were friendly", the sentiment labels of aspect "service" and "people" are negative and positive, respectively. the main challenge of absa is how to effectively capture the relationship between aspect term and its corresponding opinion expressions while preventing interference from irrelevant context words. recent studies on absa task either utilize various attention mechanisms to model the semantic relationships between aspect and its context words to obtain aspect-related sentiment representation @xcite @xcite @xcite , or leverage syntactic information derived from syntax dependency tree to model syntactic structures to capture the structural dependencies between aspect and its sentiment expressions @xcite @xcite . ## methodology in this section, we describe our proposed a2smvcl framework for absa in detail. as shown in figure 2 , the proposed a2smvcl framework mainly contains five components: 1) aspectspecific scope detection, which detects the scope of the given aspect based on word vectors in a sentence; 2) multiple graphs generation, which constructs and encodes two types of graph views for each sentence; 3) scope-assisted multi-view graph contrastive learning, which encodes different types of information while filtering out noisy signals from other aspect-specific scopes based on syntactic and semantic graphs. 4) adaptive multi-graph fusion, which fuses syntactic and semantic information, and outputs the final representations; 5) aspect-oriented sentiment classification, which classifies the sentiments of given aspect. ## related works early efforts in absa mainly use the traditional machine learning techniques, such as bag-of-words, svm, and so on, to manually extract engineered features to classify the sentiment @xcite . recently, some methods use attention mechanism to explore the word-level semantic correlations between contextual words @xcite @xcite @xcite @xcite , which aims at capturing the wordlevel semantic interaction between aspect and context. some methods use syntax dependency trees to model the syntactic relationship between aspect and its corresponding opinion. for example, zhang et al. (2019), wang et al. (2020) and liang et al. (2022) utilize aspect-oriented dependency trees to capture the syntactic relationship with graph neural network. chen et al. (2020) uses gcn to incorporate syntactic information and hardkuma-based latent graph information. xu et al. ( 2022 ) introduces the concept of scope, which outlines a structural text region related to a specific aspect. moreover, some works @xcite @xcite utilize gcn to effectively fuse syntactic and semantic information to obtain richer aspect-oriented sentiment representation. our work shares a similar syntactic and semantic information extraction approach, however we focus on designing the multi-view graph contrastive learning to capture the significant aspectdependent sentiment clues to improve absa task. ## conclusion we have re-examined the challenges encountered by existing models for absa, and pointed out the suitability of the scope-assisted graph contrastive learning for addressing these issues. accordingly, we have proposed a novel aspect-to-scope oriented multi-view contrastive learning framework to mitigate noisy interference, and leverage the correlations and differences among different sentiment polarities and different types of information (syntactic dependency and semantic correlation) extensive experiments on the six benchmark datasets have demonstrated the effectiveness and superiority of our proposed model.