Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge (2021)

Abstract: In this paper, we investigate the Aspect Category Sentiment Analysis (ACSA) task from a novel perspective by exploring a Beta Distribution guided aspect-aware graph construction based on external knowledge. That is, we are no longer entangled about how to laboriously search the sentiment clues for coarse-grained aspects from the context, but how to preferably find the words highly related to the aspects in the context and determine their importance based on the public knowledge base. In this way, the contextual sentiment clues can be explicitly tracked in ACSA for the aspects in the light of these aspect-related words. To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge. Then, we employ Beta Distribution to educe the aspect-aware weight, which reflects the importance to the aspect, for each aspect-aware word. Afterward, the aspect-aware words are served as the substitutes of the coarse-grained aspect to construct graphs for leveraging the aspect-related contextual sentiment dependencies in ACSA. Experiments on 6 benchmark datasets show that our approach significantly outperforms the state-of-the-art baseline methods.

## introduction aspect category sentiment analysis (acsa) aims to detect the sentiment polarity for a coarse-grained aspect category from a given sentence. different from the task of target-dependent or aspect term sentiment analysis, whose target or term explicitly occurs in the sentence, the aspect category in acsa does not necessarily occur in the sentence. here, the aspect category (hereinafter also referred to as aspect) generally consists of an entity e and an attribute a (i.e. e#a) or only an entity e. as shown in figure 1 , in sentence "this place is pricey, but the pizza is yummy.", there are two aspects mentioned in the sentence: "restaurant#prices" (negative) and "food#quality" (positive). many existing research efforts focus on acsa with deep learning methods to attend the significant information for the aspect category in sentiment prediction @xcite @xcite @xcite @xcite . despite promising progress made by existing methods, they are generally entangled about how to search the sentiment clues of coarse-grained aspects from the context. however, making sense of the aspectoriented sentiment words from the context purely based on the implicit aspects is a daunting task. this mostly due to 1) aspect categories generally do not manifest in the context, and 2) multiple aspects and sentiment polarities may be mentioned in the same context. on the contrary, we can exploit the aspect-related words that explicitly occur in the sentence to model the contextual sentiment information for the aspect. as the examples shown in figure 1 , there are some aspect-related words (e.g. "place", "pricey", "pizza" and "yummy") in the sentence, allowing us to explicitly leverage the sentiment dependencies with these words for identifying the sentiment polarities of the aspects. motivated by this, we investigate the acsa task from a novel perspective by proposing to construct aspect-aware graph(s) for the context with respect to the corresponding aspect. more concretely, we regard the distinct aspect as the distinct pivot and then search the aspect-related words from external knowledge, called aspect-aware words, which are served as the substitutes of the coarse-grained aspect to construct graph of the context for the specific aspect. that is, external knowledge is deployed as a bridge between implicit aspect category and the context, so as to skillfully and actively build connections between highly aspect-related context words and the specific aspect by means of a graph construction. in addition, inspired by many previous graph-based methods @xcite @xcite @xcite , weights of edges in a graph are important for graph information aggregation. moreover, based on our empirical study (as shown in figure 3 and 4 ), the contributions of aspect-aware words to the aspect are obviously different. for example, the aspectaware word "place" is more important than "pizza" to the aspect entity "restaurant". following that the main challenge of the idea evolves into how to determine the importance of aspect-aware words for the corresponding aspect, which can be leveraged as the weights of edges in a graph for learning the sentiment clues of the aspect. in the light of the knowledge base, vividly, a word can connect or not to an aspect via various routes, the successful connection probability (corresponding to the weight of an edge in a graph) can be naturally regard as a binomial distribution. we hence examine the weights of edges via modeling all the probabilities of successful connection possibility based on the prior knowledge (routes and connection information) of external knowledge by means of beta distribution @xcite , which is the conjugate prior distribution of binomial distribution. in this way, all the probabilities of aspect-aware words that connecting to the aspect could be investigated, so as to determine the optimum confidence probability (weight) of the aspect-aware word, called aspect-aware weight. subsequently, we construct aspect-aware graph(s) for each context with respect to the aspect based on the aspect-aware words paired with their weights. based on it, an aspect-aware graph convolutional network (aagcn) structure is proposed to draw contextual sentiment dependencies to the aspect for acsa. the main contributions of our work are summarized as follows: ## related work previous studies in acsa task largely pay attention to straightforwardly extract the contextual sentiment for coarse-grained aspect categories. @xcite proposed an attention-based lstm model for selectively attending the regions of the context representations. xue and li (2018) exploited a gated convolutional neural network to selectively extract aspect-specific sentiment information for sentiment prediction. xing et al. (2019) explored an aspect-aware lstm to incorporate aspect information into lstm cells for acsa. in multitask learning methods, li et al. (2020b) adopted aspect category detection task to aggregate the sentiment for the aspect from the context. chen et al. (2020) modeled document-level sentiment preference with cooperative graph attention networks for document-level acsa. cai et al. (2020) explored a hierarchical graph convolutional network to model the inner-and inter-relations for aspects in sentiment prediction. in addition, to enhance the learning ability of the model, there are a series of studies that incorporate external knowledge into the framework @xcite @xcite @xcite . among them, tian et al. (2020) modeled sentiment information at the word, polarity, and aspect level into pre-trained sentiment representation in sentiment analysis based on the automatically-mined knowledge. @xcite exploited semantic and emotion lexicons as a bridge to enable knowledge transfer across different targets in cross-target stance detection. in targeted aspect-based sentiment analysis, @xcite exploited affective knowledge to extend the classic lstm cell for simultaneously learning a target-specific attention and a global attention. ## methodology in this section, we describe our proposed aspectaware graph convolutional network (aagcn) in detail. as illustrated in figure 2 , our proposed model consists of three primary components: 1) aspect-aware words derivation, which generates a distinct series of affective words for the distinct aspect from external knowledge. 2) aspect-aware graphs construction, which constructs aspect-aware graphs of the context based on aspect-aware words. 3) aspect-aware sentiment learning, which extracts the aspect-related sentiment dependencies based on aspect-aware graphs and context representations. ## conclusion in this paper, we investigate the aspect category sentiment analysis (acsa) task from a novel perspective that learning how to preferably find the aspect-aware words that are highly related to the aspects, and educe their weights with beta distribution based on the public knowledge. the aspectaware words paired with their weights are deployed to construct aspect-aware graph(s) of the context for learning the contextual sentiment dependencies in acsa with a graph convolutional structure. experimental results on 6 benchmark datasets demonstrate the effectiveness of our proposed method.