Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and Opinions (2021)

Abstract: Product reviews contain a large number of implicit aspects and implicit opinions. However, most of the existing studies in aspect-based sentiment analysis ignored this problem. In this work, we introduce a new task, named Aspect-Category-Opinion-Sentiment (ACOS) Quadruple Extraction, with the goal to extract all aspect-category-opinion-sentiment quadruples in a review sentence and provide full support for aspect-based sentiment analysis with implicit aspects and opinions. We furthermore construct two new datasets, Restaurant-ACOS and Laptop-ACOS, for this new task, both of which contain the annotations of not only aspect-category-opinion-sentiment quadruples but also implicit aspects and opinions. The former is an extension of the SemEval Restaurant dataset; the latter is a newly collected and annotated Laptop dataset, twice the size of the SemEval Laptop dataset. We finally benchmark the task with four baseline systems. Experiments demonstrate the feasibility of the new task and its effectiveness in extracting and describing implicit aspects and implicit opinions. The two datasets and source code of four systems are publicly released at https://github.com/NUSTM/ACOS .

## introduction as a fine-grained sentiment analysis task, aspectbased sentiment analysis (absa) has received continuous attention. its core task is to extract the opinion target described by an entity and its aspect (collectively referred to as aspect) from product reviews, and identify the sentiment toward the aspect @xcite . the standard aspect-based sentiment analysis task includes two basic subtasks: aspect extraction and aspect-based sentiment classification. by integrating the two subtasks, one can identify an aspect-sentiment pair (g, s), where g is an aspect term, and s is the sentiment polarity toward the aspect. @xcite pointed out that the correlation between the aspect term and the opinion term is helpful for better absa. the following studies in this direction includes aspect-opinion co-extraction @xcite @xcite @xcite , aspect-opinion pair extraction @xcite , and aspect-opinion-sentiment triple extraction @xcite @xcite , etc. however, most of the existing studies only considered the extraction of explicit aspects and opinions, while ignored the implicit ones. in fact, product reviews contain a large amount of implicit aspects and opinions. in this work, we introduce a new task named aspect-category-opinion-sentiment (acos) quadruple extraction, with the goal to extract all aspect-category-opinion-sentiment quadruples in a review sentence, and provide full support for aspect-level sentiment analysis with implicit aspects and opinions. as shown in figure 1 , in the review sentence "looks nice and the surface is smooth, but certain apps take seconds to respond", surface is an aspect, design is its category, smooth is the opinion toward this aspect, and positive is the corresponding sentiment. the four elements are combined into an explicit quadruple surface-design-smooth-positive. in addition to that, there are two other quadruples that need to be extracted: null-design-nice-positive which contains an implicit aspect, and apps-software-null-negative which contains an implicit opinion. the new acos quadruple extraction task has the following two challenges: to address these issues, we further construct two new datasets, restaurant-acos and laptop-acos, for the new task. the former is an extension of the existing semeval restaurant dataset, based on which we add the annotation of implicit aspects, implicit opinions, and the quadruples. the latter is a brand new one collected from the amazon laptop domain. it has twice size of the semeval loptop dataset, and is annotated with quadruples containing all explicit/implicit aspects and opinions. we finally benchmark the task by establishing four baseline systems, double-propagation-acos, jet-acos, tas-bert-acos and extract-classify-acos, by adapting the representative approaches in aspect-opinion pair extraction, aspect-category-opinion triple extraction or aspect-opinion-sentiment triple extraction to acos quadruple extraction. the experiments on the two acos datasets demonstrate the feasibility of the new acos quadruple extraction task and its effectiveness in extracting and describing implicit aspects and implicit opinions. the contributions of this work can be summarized as follows: we first define the four elements of the acos quadruple extraction task based on @xcite . @xcite provided good summaries of recent tasks and terminology in absa. for simplicity, in this paper we use aspect, category, opinion and sentiment to denote aspect term, aspect category, opinion term and sentiment polarity, respectively. they are defined as follows: aspect-category-opinion-sentiment (acos) quadruple extraction is then defined as a task to extract a set of aspect-category-opinion-sentiment quadruples described in a review sentence containing n words @xmath0, . . . , w n ]: ## datasets we construct two new datasets, restaurant-acos and laptop-acos, for the acos quadruple extraction task. ## experiments we evaluate the performance of four baselines systems on two acos quadruple datasets. ## related work aspect-based sentiment analysis (absa) has drawn wide attention during the last decade. as a core task of absa, aspect-based sentiment classification (absc) which aims to detect the sentiment of a given aspect has been extensively studied in the literature @xcite @xcite @xcite @xcite @xcite @xcite @xcite . in recent years, on the basis of traditional absc, a series of expansion tasks have appeared in this field. we divide these work into the following four categories: aspect-sentiment pair extraction. it also can be viewed as joint aspect extraction and absc. @xcite first explored the opendomain aspect-sentiment extraction task by designing a variety of conditional random fieldbased models based on traditional discrete features. with the recent trend of deep learning, researchers have proposed various neural pipeline approaches @xcite or joint learning approaches for this task @xcite @xcite . aspect-opinion pair extraction. @xcite first addressed the task in a pipeline manner. @xcite proposed to extract aspectopinion pairs with a double-channel recurrent network while taking the correlation between aspects and opinions into consideration. @xcite designed a span-based multi-task learning framework to extract aspect-opinion pairs jointly. the work on aspect-opinion co-extraction @xcite @xcite can be viewed as the first stage of aspect-opinion pair extraction. ## conclusions and future work in this paper, we introduce a new task, aspect-category-opinion-sentiment (acos) quadruple extraction, aiming to systematically address the implicit aspect/opinion problem. we construct two new datasets for this task, with acos annotations including implicit aspects and implicit opinions. we finally benchmark the task with four baseline systems. experiments demonstrate the advantages of the new task in aspect-based sentiment analysis with implicit aspects/opinions. the focus of this paper is the introduction of the new task and datasets. the proposed four baseline systems are relatively simple and leave much room for further improvements. we welcome future work proposing stronger models on this task. we also welcome the usage of our datasets on the other absa tasks.