Better Queries for Aspect-Category Sentiment Classification (2020)

Abstract: Aspect-category sentiment classification (ACSC) aims to identify the sentiment polarities towards the aspect categories mentioned in a sentence. Because a sentence often mentions more than one aspect category and expresses different sentiment polarities to them, finding aspect category-related information from the sentence is the key challenge to accurately recognize the sentiment polarity. Most previous models take both sentence and aspect category as input and query aspect category-related information based on the aspect category. However, these models represent the aspect category as a context-independent vector called aspect embedding, which may not be effective enough as a query. In this paper, we propose two contextualized aspect category representations, Contextualized Aspect Vector (CAV) and Contextualized Aspect Matrix (CAM). Specifically, we use the coarse aspect category-related information found by the aspect category detection task to generate CAV or CAM. Then the CAV or CAM as queries are used to search for fine-grained aspect category-related information like aspect embedding by aspect-category sentiment classification models. In experiments, we integrate the proposed CAV and CAM into several representative aspect embedding-based aspect-category sentiment classification models. Experimental results on the SemEval-2014 Restaurant Review dataset and the Multi-Aspect Multi-Sentiment dataset demonstrate the effectiveness of CAV and CAM.

## introduction sentiment analysis @xcite is an important task in natural language processing (nlp). it deals with the computational treatment of opinion, sentiment, and subjectivity in text. aspectbased sentiment analysis @xcite @xcite is a branch of sentiment analysis and aspect-category sentiment analysis (acsa) is a subtask of it. in acsa, there are a predefined set of aspect categories, and a predefined set of sentiment polarities. given a sentence, the task aims to predict the aspect categories mentioned in the sentence and the corresponding sentiments. therefore, acsa contains two subtasks: aspect category detection (acd) that detects aspect categories in a sentence and aspect-category sentiment classification (acsc) that categorizes the sentiment polarities with respect to the detected aspect categories. figure 1 shows an example, "staffs are not that friendly, but the taste covers all.". acd detects the sentence mentions two aspect categories: service and food, and acsc predicts the sentiment polarities to them: negative and positive respectively. in this work, we focus on acsc, while acd as an auxiliary task is used to find coarse aspect category-related information for the acsc task. because a sentence often mentions more than one aspect category and expresses different sentiment polarities to them, to accurately recognize the sentiment polarities, most previous models @xcite @xcite @xcite @xcite @xcite @xcite take both sentence and aspect category as input and query aspect category-related information based on the aspect staffs are not that friendly, but the taste covers all. <service, @xmath0, @xmath1, then generate aspect category-specific representations for aspect-category sentiment classification. however, these models represent the aspect category as a context-independent vector called aspect embedding (ae). these models can be called aspect embedding-based models. since aspect embedding only contains the global information of aspect category and loses the context-dependent information, it is semantically far away from the words in the sentence, and may not be effective enough as a query to search for aspect category-related information for the acsc task. these models may be improved by replacing the aspect embedding with context-dependent aspect category representations. the hierarchical attention (heat) network @xcite used context-dependent aspect category representations to search for aspect category-related information for the acsc task and obtained better performance. the context-dependent aspect category representations are generated by concatenating the aspect embedding and the aspect term representation in a sentence. an aspect term is a word or phrase that appears in the sentence explicitly indicating an aspect category. for the example in figure 1 , the aspect terms are "staffs" and "taste" indicating aspect category service and food respectively. however, the heat network requires aspect term annotation information that the data for acsc usually does not have. moreover, the heat network ignores the situation where the aspect category is mentioned implicitly in sentences without any aspect term, making aspect category representations degenerate to context-independent representations in this situation. in this paper, we propose two novel contextualized aspect category representations, contextualized aspect vector (cav) and contextualized aspect matrix (cam). cav or cam contain context-dependent information even though there are no aspect terms in sentences, and aspect term annotation information is not required to generate them. concretely, we use the coarse aspect category-related information found by the acd task to generate cav or cam. then cav or cam as queries are used to search for fine-grained aspect category-related information like aspect embedding by aspect-category sentiment classification models. specifically, we first use an attention-based aspect category classifier to obtain the weights of the words in a sentence, which indicate the degree of correlation between the aspect categories and the words. then, we get cav by combining the weighted sum of the word representations with corresponding aspect embedding. that is to say, cav contains two kinds of representations of an aspect category: context-independent representation and context-dependent representation, which capture global information and local information respectively. since cav may lose details of the words, we also propose an aspect category matrix representation, called contextualized aspect matrix (cam), which is a not-sum version of cav. in summary, the main contributions of our work can be summarized as follows: in this section, we first present a brief review about aspect-category sentiment classification. then, we show the related study on context-aware aspect embedding that is a kind of context-dependent aspect category representation for targeted aspect based sentiment analysis (tabsa). ## method in this section, we describe our proposed two contextualized aspect category representations, contextualized aspect vector (cav) and contextualized aspect matrix (cam), in detail. motivated by the process that people search for information through search engines: before finding the result they want, they usually try different words and adjust their queries based on previous results, the process to generate cav or cam consists of two steps. in the first step, the acd task as an auxiliary task is used to find coarse aspect category-related information. in the second step, the coarse aspect category-related information is used to optimize original query (e.g. aspect embedding). specifically, an attention-based aspect category classifier generates the weights of the words in a sentence about all predefined categories. then the weights are used to generate cav and cam. the framework of our proposed method is demonstrated in figure 2 . ## conclusion in this paper, we propose two novel contextualized aspect category representations, contextualized aspect vector (cav) and contextualized aspect matrix (cam). they include both the global information and local information about the aspect category and are better queries to search for aspect categoryrelated information for the acsc task. moreover, cav or cam contain context-dependent information even though there are no aspect terms in sentences, and aspect term annotation information is not required to generate them. we experiment with several representative aspect embedding-based models by replacing the aspect embedding with cav or cam. experimental results on the semeval-2014 restaurant dataset and the multi-aspect multi-sentiment (mams) dataset show that the variants with cav or cam obtain better performance. in future works, we will explore the performance of cav and cam with knowledge from open knowledge graphs on the acsc task.