Aspect-Enhanced Prompting Method for Unsupervised Domain Adaptation in Aspect-Based Sentiment Analysis
This study proposes an Aspect-Enhanced Prompting (AEP) method for unsupervised Multi-Source Domain Adaptation in Aspect Sentiment Classification, where data from the target domain are completely unavailable for model training. The proposed AEP is based on two generative language models: one generate...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2078-2489/16/5/411 |
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| author | Binghan Lu Kiyoaki Shirai Natthawut Kertkeidkachorn |
| author_facet | Binghan Lu Kiyoaki Shirai Natthawut Kertkeidkachorn |
| author_sort | Binghan Lu |
| collection | DOAJ |
| description | This study proposes an Aspect-Enhanced Prompting (AEP) method for unsupervised Multi-Source Domain Adaptation in Aspect Sentiment Classification, where data from the target domain are completely unavailable for model training. The proposed AEP is based on two generative language models: one generates a prompt from a given review, while the other follows the prompt and classifies the sentiment of an aspect. The first model extracts Aspect-Related Features (ARFs), which are words closely related to the aspect, from the review and incorporates them into the prompt in a domain-agnostic manner, thereby directing the second model to identify the sentiment accurately. Our framework incorporates an innovative rescoring mechanism and a cluster-based prompt expansion strategy. Both are intended to enhance the robustness of the generation of the prompt and the adaptability of the model to diverse domains. The results of experiments conducted on five datasets (Restaurant, Laptop, Device, Service, and Location) demonstrate that our method outperforms the baselines, including a state-of-the-art unsupervised domain adaptation method. The effectiveness of both the rescoring mechanism and the cluster-based prompt expansion is also validated through an ablation study. |
| format | Article |
| id | doaj-art-f30a5167237345a19a529d4dff52f3d8 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-f30a5167237345a19a529d4dff52f3d82025-08-20T03:47:59ZengMDPI AGInformation2078-24892025-05-0116541110.3390/info16050411Aspect-Enhanced Prompting Method for Unsupervised Domain Adaptation in Aspect-Based Sentiment AnalysisBinghan Lu0Kiyoaki Shirai1Natthawut Kertkeidkachorn2Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa 923-1292, JapanGraduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa 923-1292, JapanGraduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa 923-1292, JapanThis study proposes an Aspect-Enhanced Prompting (AEP) method for unsupervised Multi-Source Domain Adaptation in Aspect Sentiment Classification, where data from the target domain are completely unavailable for model training. The proposed AEP is based on two generative language models: one generates a prompt from a given review, while the other follows the prompt and classifies the sentiment of an aspect. The first model extracts Aspect-Related Features (ARFs), which are words closely related to the aspect, from the review and incorporates them into the prompt in a domain-agnostic manner, thereby directing the second model to identify the sentiment accurately. Our framework incorporates an innovative rescoring mechanism and a cluster-based prompt expansion strategy. Both are intended to enhance the robustness of the generation of the prompt and the adaptability of the model to diverse domains. The results of experiments conducted on five datasets (Restaurant, Laptop, Device, Service, and Location) demonstrate that our method outperforms the baselines, including a state-of-the-art unsupervised domain adaptation method. The effectiveness of both the rescoring mechanism and the cluster-based prompt expansion is also validated through an ablation study.https://www.mdpi.com/2078-2489/16/5/411aspect-based sentiment analysisunsupervised domain adaptationmulti-source domain adaptationtext generation modelprompt engineering |
| spellingShingle | Binghan Lu Kiyoaki Shirai Natthawut Kertkeidkachorn Aspect-Enhanced Prompting Method for Unsupervised Domain Adaptation in Aspect-Based Sentiment Analysis Information aspect-based sentiment analysis unsupervised domain adaptation multi-source domain adaptation text generation model prompt engineering |
| title | Aspect-Enhanced Prompting Method for Unsupervised Domain Adaptation in Aspect-Based Sentiment Analysis |
| title_full | Aspect-Enhanced Prompting Method for Unsupervised Domain Adaptation in Aspect-Based Sentiment Analysis |
| title_fullStr | Aspect-Enhanced Prompting Method for Unsupervised Domain Adaptation in Aspect-Based Sentiment Analysis |
| title_full_unstemmed | Aspect-Enhanced Prompting Method for Unsupervised Domain Adaptation in Aspect-Based Sentiment Analysis |
| title_short | Aspect-Enhanced Prompting Method for Unsupervised Domain Adaptation in Aspect-Based Sentiment Analysis |
| title_sort | aspect enhanced prompting method for unsupervised domain adaptation in aspect based sentiment analysis |
| topic | aspect-based sentiment analysis unsupervised domain adaptation multi-source domain adaptation text generation model prompt engineering |
| url | https://www.mdpi.com/2078-2489/16/5/411 |
| work_keys_str_mv | AT binghanlu aspectenhancedpromptingmethodforunsuperviseddomainadaptationinaspectbasedsentimentanalysis AT kiyoakishirai aspectenhancedpromptingmethodforunsuperviseddomainadaptationinaspectbasedsentimentanalysis AT natthawutkertkeidkachorn aspectenhancedpromptingmethodforunsuperviseddomainadaptationinaspectbasedsentimentanalysis |