A novel textual data augmentation method for identifying comparative text from user-generated content

作者：

Highlights：

• A framework is proposed for comparative text identification from user-generated content.

• A syntax-aware and sentiment polarity-aware text augmentation method is proposed.

• A series of experiments verify the feasibility and effectiveness of the framework.

摘要

•A framework is proposed for comparative text identification from user-generated content.•A syntax-aware and sentiment polarity-aware text augmentation method is proposed.•A series of experiments verify the feasibility and effectiveness of the framework.

论文关键词：Deep learning,Textual data augmentation,Substitute word generation,Comparative text identification

论文评审过程：Received 10 November 2021, Revised 18 February 2022, Accepted 22 March 2022, Available online 24 March 2022, Version of Record 28 March 2022.

论文官网地址：https://doi.org/10.1016/j.elerap.2022.101143