Affective Knowledge Enhanced Multiple-Graph Fusion Networks for Aspect-based Sentiment Analysis (2022)

Abstract: Aspect-based sentiment analysis aims to identify sentiment polarity of social media users toward different aspects. Most recent methods adopt the aspect-centric latent tree to connect aspects and their corresponding opinion words, thinking that would facilitate establishing the relationship between aspects and opinion words.However, these methods ignore the roles of syntax dependency relation labels and affective semantic information in determining the sentiment polarity, resulting in the wrong prediction.In this paper, we propose a novel multi-graph fusion network (MGFN) based on latent graph to leverage the richer syntax dependency relation label information and affective semantic information of words.Specifically, we construct a novel syntax-aware latent graph (SaLG) to fully leverage the syntax dependency relation label information to facilitate the learning of sentiment representations. Subsequently, a multi-graph fusion module is proposed to fuse semantic information of surrounding contexts of aspects adaptively. Furthermore, we design an affective refinement strategy to guide the MGFN to capture significant affective clues. Extensive experiments on three datasets demonstrate that our MGFN model outperforms all state-of-the-art methods and verify the effectiveness of our model.

## introduction sentiment analysis has been a popular research subject in natural language processing. aspect-based sentiment analysis (absa) @xcite is a fine-grained sentiment analysis task. for example, given a sentence "the menu is limited but the dishes are excellent.", there are two aspects mentioned in the sentence and the sentiment polarity of aspects "menu" and "dishes" are negative and positive, respectively. generally, absa task is formulated as predicting the polarity of a given * the first two authors contributed equally to this work. ## related work aspect-based sentiment analysis: sentiment analysis is one of the most active research areas in natural language processing @xcite , and is widely studied in qa system @xcite , stance detection @xcite , recommendation system @xcite , and event detection @xcite . aspect-based sentiment analysis (absa) is first proposed by @xcite to refine sentiment analysis, which aims to detect fine-grained sentiments towards different aspects. early efforts on absa utilizes attention-based neural models to model semantic interactions @xcite . some other efforts @xcite @xcite try to explicitly establish the syntactic dependency connections between words. graph neural networks: recently, graph neural networks (gnns) @xcite have received growing attention and successfully used in many applications such as action recognition @xcite , relation extraction @xcite and scene image generation @xcite . @xcite innovatively utilized graph convolution networks (gcns) for text classification in natural language process field. for absa, @xcite used gcns to encode dependency information of syntactic dependency parse tree. @xcite proposed a dependency graph enhanced dual-transformer network(dgedt) to allow the dependency graph to guide the representation learning of the transformer encoder. @xcite constructed the aspect-oriented dependency trees by which reshaped the ordinary dependency parse tree to root it at aspect using manual rules. @xcite used the probability matrix with all dependency structures of input sentence from off-the-shelf dependency parser to alleviate inaccurate parse problem and integrated syntactic and semantic information. more recently, several teams have explored to construct latent graph that can adaptively capture the relation between words of the sentence in an end-to-end fashion. chen et al. (2020) constructed a latent graph sampled from the hard-kuma distribution, and combined a dependency parse graph with it to generate task-specific representation. @xcite utilized a variant of kirchhoff's matrix-tree theorem to induce the task-specific aspect-centric latent dependency tree. ## conclusion in this paper, we propose an mgfn model to address the disadvantages of latent graph based models for aspect-based sentiment analysis. we construct a novel salg to leverage the richer syntax dependency relation label information, and adaptively fuse the semantic information from seg into salg to facilitate the learning of sentiment representation. moreover, to capture more significant affective clues from surrounding contexts, we propose an affective refinement strategy in multi-graph fusion module. this strategy can guide mgfn to pay more attention to the opinion expressions of aspects. extensive experiments on three datasets show that our model achieves the best performance.