Alignment-Based Pseudo-Label Generation With Collaborative Filtering Mechanism for Enhanced Cross-Domain Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) in areas such as online shopping and restaurants can effectively facilitate specific service improvements. However, ABSA performance heavily relies on high-quality labeled data, posing a major challenge in data-scarce domains. To solve the data scarcity problem...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10697156/ |
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| author | Yadi Xu Noor Farizah Ibrahim |
| author_facet | Yadi Xu Noor Farizah Ibrahim |
| author_sort | Yadi Xu |
| collection | DOAJ |
| description | Aspect-based sentiment analysis (ABSA) in areas such as online shopping and restaurants can effectively facilitate specific service improvements. However, ABSA performance heavily relies on high-quality labeled data, posing a major challenge in data-scarce domains. To solve the data scarcity problem, existing cross-domain ABSA research employs semi-unsupervised learning using pseudo-labels. Unfortunately, low-quality pseudo-labels may mislead model learning, resulting in suboptimal outcomes. Two key questions are raised in this paper, how to improve the quality of pseudo-label generation and how to accurately filter invalid pseudo-labels. First, although traditional adversarial training aims to improve model generalization, its instability and convergence difficulties hinder its effectiveness in managing complex inter-domain differences. To address this challenge, we introduce a scene-style pre-alignment strategy. This method effectively reduces style feature differences between domains and solves adversarial training limitations in terms of convergence and stability, thus improving the quality of pseudo-tag generation. Secondly, we propose a pseudo-label collaborative filtering mechanism, which focuses on filtering labels and texts that would mislead the model by comparing the scene feature similarity between the pseudo-label-based generated texts and target domain texts. Our approach achieves significant performance gains on several public datasets, demonstrating that the combination of aligned pre-training and adversarial training, as well as collaborative filtering mechanisms, indeed improves the model’s domain adaptation. |
| format | Article |
| id | doaj-art-e1844e08e4c34497b1124711d9a9f076 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e1844e08e4c34497b1124711d9a9f0762025-08-20T02:18:46ZengIEEEIEEE Access2169-35362024-01-011215561815563110.1109/ACCESS.2024.346987210697156Alignment-Based Pseudo-Label Generation With Collaborative Filtering Mechanism for Enhanced Cross-Domain Aspect-Based Sentiment AnalysisYadi Xu0https://orcid.org/0009-0009-7926-1935Noor Farizah Ibrahim1https://orcid.org/0000-0002-6319-5162School of Computer Science, Universiti Sains Malaysia, Penang, MalaysiaSchool of Computer Science, Universiti Sains Malaysia, Penang, MalaysiaAspect-based sentiment analysis (ABSA) in areas such as online shopping and restaurants can effectively facilitate specific service improvements. However, ABSA performance heavily relies on high-quality labeled data, posing a major challenge in data-scarce domains. To solve the data scarcity problem, existing cross-domain ABSA research employs semi-unsupervised learning using pseudo-labels. Unfortunately, low-quality pseudo-labels may mislead model learning, resulting in suboptimal outcomes. Two key questions are raised in this paper, how to improve the quality of pseudo-label generation and how to accurately filter invalid pseudo-labels. First, although traditional adversarial training aims to improve model generalization, its instability and convergence difficulties hinder its effectiveness in managing complex inter-domain differences. To address this challenge, we introduce a scene-style pre-alignment strategy. This method effectively reduces style feature differences between domains and solves adversarial training limitations in terms of convergence and stability, thus improving the quality of pseudo-tag generation. Secondly, we propose a pseudo-label collaborative filtering mechanism, which focuses on filtering labels and texts that would mislead the model by comparing the scene feature similarity between the pseudo-label-based generated texts and target domain texts. Our approach achieves significant performance gains on several public datasets, demonstrating that the combination of aligned pre-training and adversarial training, as well as collaborative filtering mechanisms, indeed improves the model’s domain adaptation.https://ieeexplore.ieee.org/document/10697156/Aspect-based sentiment analysisscene-style alignmentadversarial trainingpseudo-labelcollaborative filtering mechanisms |
| spellingShingle | Yadi Xu Noor Farizah Ibrahim Alignment-Based Pseudo-Label Generation With Collaborative Filtering Mechanism for Enhanced Cross-Domain Aspect-Based Sentiment Analysis IEEE Access Aspect-based sentiment analysis scene-style alignment adversarial training pseudo-label collaborative filtering mechanisms |
| title | Alignment-Based Pseudo-Label Generation With Collaborative Filtering Mechanism for Enhanced Cross-Domain Aspect-Based Sentiment Analysis |
| title_full | Alignment-Based Pseudo-Label Generation With Collaborative Filtering Mechanism for Enhanced Cross-Domain Aspect-Based Sentiment Analysis |
| title_fullStr | Alignment-Based Pseudo-Label Generation With Collaborative Filtering Mechanism for Enhanced Cross-Domain Aspect-Based Sentiment Analysis |
| title_full_unstemmed | Alignment-Based Pseudo-Label Generation With Collaborative Filtering Mechanism for Enhanced Cross-Domain Aspect-Based Sentiment Analysis |
| title_short | Alignment-Based Pseudo-Label Generation With Collaborative Filtering Mechanism for Enhanced Cross-Domain Aspect-Based Sentiment Analysis |
| title_sort | alignment based pseudo label generation with collaborative filtering mechanism for enhanced cross domain aspect based sentiment analysis |
| topic | Aspect-based sentiment analysis scene-style alignment adversarial training pseudo-label collaborative filtering mechanisms |
| url | https://ieeexplore.ieee.org/document/10697156/ |
| work_keys_str_mv | AT yadixu alignmentbasedpseudolabelgenerationwithcollaborativefilteringmechanismforenhancedcrossdomainaspectbasedsentimentanalysis AT noorfarizahibrahim alignmentbasedpseudolabelgenerationwithcollaborativefilteringmechanismforenhancedcrossdomainaspectbasedsentimentanalysis |