Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment Classification (2022)

Abstract: Aspect-based sentiment analysis (ABSA) has drawn more and more attention because of its extensive applications. However, towards the sentence carried with more than one aspect, most existing works generate an aspect-specific sentence representation for each aspect term to predict sentiment polarity, which neglects the sentiment relationship among aspect terms. Besides, most current ABSA methods focus on sentences containing only one aspect term or multiple aspect terms with the same sentiment polarity, which makes ABSA degenerate into sentence-level sentiment analysis. In this paper, to deal with this problem, we construct a heterogeneous graph to model inter-aspect relationships and aspect-context relationships simultaneously and propose a novel Composition-based Heterogeneous Graph Multi-channel Attention Network (CHGMAN) to encode the constructed heterogeneous graph. Meanwhile, we conduct extensive experiments on three datasets: MAMSATSA, Rest14, and Laptop14, experimental results show the effectiveness of our method.

## introduction aspect-based sentiment analysis (absa) is a sentiment analysis task, which aims to predict the sentiment polarity (e.g., positive, negative, neu-tral) towards the given aspect term in a sentence. for example, there are two aspect terms decor and food in the sentence: the decor is not a special at all but their amazing food makes up for it, and these two aspect terms will be assigned with neg-ative and positive sentiment polarities respectively. since the sentiments expressed by these two aspect terms are opposite in polarity, it is unreasonable to assign a sentence-level sentiment polarity. in this regard, absa can provide more detailed sentimental predictions compared with sentencelevel sentiment analysis. in recent years, neural network-based methods @xcite @xcite are used to tackle absa task and achieve promising performance. subsequently, with the rise of contextual embedding, contextualized language models, such as bert @xcite , are introduced into absa and boost performance. additionally, in order to utilize the dependency tree information, a lot of graphbased methods @xcite @xcite are proposed to tackle this task by encoding both contextual and dependency information simultaneously. however, most of these methods focus on the datasets, in which most sentences consist of only one aspect or multiple aspects with the same sentiment polarity. under these circumstances, aspectbased sentiment analysis degenerates into sentencelevel sentiment analysis. existing absa methods based on these datasets can hardly adapt to the situation, where multiple aspect terms in a sentence with multiple different sentiment polarities. thus, the fine-grained sentiment analysis task derived from absa towards the multi-aspect multi-sentiment situation, multi-aspect multi-sentiment classification (mamsc), is worthy of exploration @xcite . mamsc is more challenging than absa, and each sentence contains at least two aspects with different sentiment polarities in mamsc. besides, most existing methods neglect to consider inter-aspect relationships when predicting sentiment polarity. especially in mamsc, the main challenge is that the sentiment polarities of some aspects in a given sentence can not be judged solely relied on the context and may be guided by the sentiments of other aspects. inter-aspect relationships will play an essential role in identifying sentiment polarity in this case. though intergcn @xcite claims to have considered inter-aspect relationships, what it considered is merely the distance relationship among aspects and neglecting the semantic relationship between every two aspects, which is essential when indicating the relationship between two aspects. furthermore, in-tergcn ignores the heterogeneity of aspect terms and context words and the different importance of surrounding nodes when aggregating information. to sum up, absa faces the following challenges: (1) do not pay special attention to the multiaspect multi-sentiment situation; (2) do not incorporate semantic relationships among aspects into predicting sentiment polarity; (3) do not introduce heterogeneity and attention mechanism when using graph-based models to predict sentiment polarity. to deal with the challenges mentioned above, we first construct a heterogeneous graph with distinct node and edge types to represent inter-aspect relationships, aspect-context relationships, and node heterogeneity simultaneously in a concrete way. unlike intergcn, we treat aspect terms and context words as different types of nodes, and meanwhile, the edges in our heterogeneous graph are attributed. we utilize dependency tags as the attributes of aspect-context edges. the shortest dependency path between each two aspect terms is the representation of each inter-aspect edge attribute, which signifies the semantic relationship between these two aspect terms. then we propose a novel model called composition-based heterogeneous graph multi-channel attention network (chg-man) to capture important information conveyed by constructed heterogeneous graph. our proposed chgman equipped with a multi-channel attention mechanism can take edge features, node types, and node features into account concurrently and aggregate composition information of nodes and node types in terms of importance, which is conducive to capturing the information expressed by our heterogeneous graph and making the correct predictions. the main contributions of this paper can be summarized as follows: heterogeneous graph: to the best of our knowledge, this is the first attempt to model multi-aspect multi-sentiment classification with heterogeneous graph networks to capture both inter-aspect relationships and context-aspect relationships. ## methodology multi-aspect multi-sentiment classification can be formulated as follows: given a sentence s with n words {w 1 , w 2 , ..., w n } and a given aspect term set @xmath0, a 2 , ..., a κ } containing κ aspect terms. and a @xmath1, w a φ+1 , ..., w a φ+τ } with τ ∈ [1, n -φ] is a sub-string of sentence s, which denotes one element of the given aspect term set a. the annotation a represents aspect. this task aims at predicting the sentiment polarity y {positive, neutral, negative} expressed on each aspect term included in the given aspect term set a of the sentence s. as shown in figure 1 (left), our proposed methods consist of three blocks: (1) encoding block; (2) heterogeneous graph construction block; (3) prediction block. in section 3.1 to 3.3, we detail each block. ## conclusion in this paper, we construct a heterogeneous graph for mamsc and propose a novel chgman to tackle the heterogeneous graph. our model predicts sentiment polarities by incorporating interaspect relationships, aspect-context relationships, and node heterogeneity. moreover, our experiments prove the effectiveness of our method.