Transfer Learning With Document-Level Data Augmentation for Aspect-Level Sentiment Classification

Abstract

Aspect-level sentiment classification (ASC) seeks to reveal the emotional tendency of a designated aspect of a text. Some researchers have recently tried to exploit large amounts of document-level sentiment classification (DSC) data available to help improve the performance of ASC models through transfer learning. However, these studies often ignore the difference in sentiment distribution between document-level and aspect-level data without preprocessing the document-level knowledge. Our study provides a transfer learning with document-level data augmentation (TL-DDA) framework to transfer more accurate document-level knowledge to the ASC model by means of document-level data augmentation and attention fusion . First, we use document data selection and text concatenation to produce document-level data with various sentiment distributions. The augmented document data is then utilized for pre-training a well-designed DSC model. Finally, after attention adjustment , we fuse the word attention obtained from this DSC model into the ASC model. Results of experiments utilizing two publicly available datasets suggest that TL-DDA is reliable.