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- Title
Cross-Domain End-To-End Aspect-Based Sentiment Analysis with Domain-Dependent Embeddings.
- Authors
Tian, Yingjie; Yang, Linrui; Sun, Yunchuan; Liu, Dalian.
- 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.
- Subjects
SENTIMENT analysis; LEARNING strategies; USER-generated content
- Publication
Complexity, 2021, p1
- ISSN
1076-2787
- Publication type
Article
- DOI
10.1155/2021/5529312