Cross-Domain End-To-End Aspect-Based Sentiment Analysis with Domain-Dependent Embeddings. (15th March 2021)

• Record Type: Journal Article
• Authors: Tian, Yingjie; Yang, Linrui; Sun, Yunchuan; Liu, Dalian.
• Other Names: Lv Zhihan Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-03-15
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/5529312
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : With the development of sentiment analysis, studies have been gradually classified based on different researched candidates. Among them, aspect-based sentiment analysis plays an important role in subtle opinion mining for online reviews. It used to be treated as a group of pipeline tasks but has been proved to be analysed well in an end-to-end model recently. Due to less labelled resources, the need for cross-domain aspect-based sentiment analysis has started to get attention. However, challenges exist when seeking domain-invariant features and keeping domain-dependent features to achieve domain adaptation within a fine-grained task. This paper utilizes the domain-dependent embeddings and designs the model CD-E2EABSA to achieve cross-domain aspect-based sentiment analysis in an end-to-end fashion. The proposed model utilizes the domain-dependent embeddings with a multitask learning strategy to capture both domain-invariant and domain-dependent knowledge. Various experiments are conducted and show the effectiveness of all components on two public datasets. Also, it is also proved that as a cross-domain model, CD-E2EABSA can perform better than most of the in-domain ABSA methods.