MOIT: A Novel task for mining opinions towards implicit targets

Abstract

The extraction of opinions and their corresponding targets has gained significant interest recently, as it offers valuable insights into Opinion Mining (OM) at a granular level. Opinion and target terms to be extracted by existing OM tasks need to be explicitly present in reviews. Targets that are not present but implied in contextual semantics, are neglected by existing OM tasks, even though an investigation reported that about 60% of reviews contain implicit targets. To enable implicit target extraction, a novel task named Mining Opinions towards Implicit Targets (MOIT) under the fine-grained OM, is proposed to extract both opinions and their corresponding implicit targets, enabling a more comprehensive analysis of reviews. To set up the basis for follow-up research on MOIT, two large-scale datasets were constructed as resources in two languages, where the Chinese dataset was built from scratch via a standard human annotation process, and the English dataset was built semi-automatically through machine translation and manual checking. Furthermore, three baseline models adapting three representative paradigms of information extraction, namely sequence labeling, question answering, and text generation, were proposed to solve MOIT. Extensive experiments demonstrated the effectiveness of the models. The proposed MOIT task extends the field of OM research, and the datasets and models establish a foundation for future studies in this area.