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

ACL 2021  ·  Hongjie Cai, Rui Xia, Jianfei Yu ·

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 \url{https://github.com/NUSTM/ACOS}.

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Datasets


Introduced in the Paper:

Restaurant-ACOS Laptop-ACOS

Used in the Paper:

ACOS
Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Aspect-Based Sentiment Analysis (ABSA) ACOS Extract-Classify F1 (Laptop) 36.42 # 6
F1 (Restaurant) 43.77 # 6
Aspect-Category-Opinion-Sentiment Quadruple Extraction Laptop-ACOS Extract-Classify-ACOS F1 35.8 # 1
Aspect-Category-Opinion-Sentiment Quadruple Extraction Restaurant-ACOS Extract-Classify-ACOS F1 44.61 # 1

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