Product reviews contain a large number of implicit aspects and 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 further construct two new datasets Restaurant-ACOS and Laptop-ACOS for this new task. The former is an extension of the SemEval Restaurant dataset; the latter is a brand new Laptop dataset with much larger size than the Se-mEval Laptop dataset. Both contain the annotations of not only aspect-category-opinionsentiment quadruples but also implicit aspects and opinions. We finally benchmark the task with four baseline systems. Experiments demonstrate the feasibility of the new task and its advantage in extracting and describing implicit aspects and implicit opinions in ABSA. The two datasets and source code of four systems are publicly released at https:

Most of the aspect based sentiment analysis research aims at identifying the sentiment polarities toward some explicit aspect terms while ignores implicit aspects in text. To capture both explicit and implicit aspects, we focus on aspect-category based sentiment analysis, which involves joint aspect category detection and category-oriented sentiment classification. However, currently only a few simple studies have focused on this problem. The shortcomings in the way they defined the task make their approaches difficult to effectively learn the inner-relations between categories and the inter-relations between categories and sentiments. In this work, we re-formalize the task as a category-sentiment hierarchy prediction problem, which contains a hierarchy output structure to first identify multiple aspect categories in a review sentence, and jointly predict the sentiment for each of the identified categories. Specifically, we propose a Hierarchical Graph Convolutional Network (Hier-GCN), where a lower-level GCN is to model the inner-relations among multiple categories, and the higher-level GCN is to capture the inter-relations between aspect categories and sentiments. Extensive evaluations demonstrate that our hierarchy output structure is superior over existing ones, and the Hier-GCN model consistently achieves the best results on four benchmarks.