A Knowledge-Enhanced and Topic-Guided Domain Adaptation Model for Aspect-Based Sentiment Analysis

Cross-domain aspect-based sentiment analysis has recently attracted significant attention, which can effectively alleviate the problem of lacking large-scale labeled data for supervised learning based methods. Most of current methods mainly focus on extracting domain-shared syntactic features to con...

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Bibliographic Details
Published in:IEEE transactions on affective computing 2024-04, Vol.15 (2), p.709-721
Main Authors: Zeng, Yushi, Wang, Guohua, Ren, Haopeng, Cai, Yi, Leung, Ho-Fung, Li, Qing, Huang, Qingbao
Format: Article
Language:English
Subjects:
Adaptation
Adaptation models
cross-domain
Data mining
Errors
Feature extraction
knowledge graph
Knowledge graphs
Knowledge management
Knowledge representation
Modules
Portable computers
Sentiment analysis
Supervised learning
Syntactics
Task analysis
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Summary:Cross-domain aspect-based sentiment analysis has recently attracted significant attention, which can effectively alleviate the problem of lacking large-scale labeled data for supervised learning based methods. Most of current methods mainly focus on extracting domain-shared syntactic features to conduct the domain adaptation. Due to the language and syntax are diverse between domains, these methods lack generalization and even lead to syntactic transfer errors. External knowledge graphs have rich domain commonsense and share the relational structures between source and target domains. The domain-shared relational structure can effectively bridge the gap across domains and solve the problem of syntactic transfer errors. Moreover, not all the introduced external knowledge is equally important for the cross-domain aspect-based sentiment analysis. Motivated by these, we propose a knowledge-enhanced and topic-guided cross domain aspect-based sentiment analysis model with the domain-shared commonsense relational structure learning module and the topic-guided knowledge attention module. Extensive experiments are conducted and the experimental results evaluate the effectiveness of our proposed model.
ISSN:1949-3045
1949-3045
DOI:10.1109/TAFFC.2023.3292213