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

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: //github.com/NUSTM/ACOS.