DSC_NEXT: Document-Level Sentiment ClassificatioN with EXTensional-Information

Abstract

Document-level sentiment classification is to assign an overall sentiment polarity to an opinion document. Some researchers have already realized that, in addition to document texts, extensional-information such as product features and user preferences can be quite useful. Many previous studies represent them as ID-type extensional-information and incorporate them into deep learning models. However, they ignore the descriptive extensional information that is also useful for document representations. This paper covers the following aspects: (1) introduces the Description of Opinion Target (DOT), a new extensional-information for document-level sentiment classification, (2) builds the Document-level Sentiment ClassificatioN with EXTensional-information (DSC_NEXT) dataset which consists of three datasets: IMDB_NEXT, Yelp_NEXT and CMRDB_NEXT and (3) validates the effectiveness of DOT by performing experiments based on current state-of-the-art (SOTA) document-level sentiment analysis methods. Implications for using extensional-information in neural network models are also considered.