Exploring ChatGPT-Based Augmentation Strategies for Contrastive Aspect-Based Sentiment Analysis
Article
Xu, Lingling, Xie, Haoran, Qin, S. Joe, Wang, Fu Lee and Tao, Xiaohui. 2025. "Exploring ChatGPT-Based Augmentation Strategies for Contrastive Aspect-Based Sentiment Analysis." IEEE Intelligent Systems. 40 (1), pp. 69-76. https://doi.org/10.1109/MIS.2024.3508432
Article Title | Exploring ChatGPT-Based Augmentation Strategies for Contrastive Aspect-Based Sentiment Analysis |
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ERA Journal ID | 4426 |
Article Category | Article |
Authors | Xu, Lingling, Xie, Haoran, Qin, S. Joe, Wang, Fu Lee and Tao, Xiaohui |
Journal Title | IEEE Intelligent Systems |
Journal Citation | 40 (1), pp. 69-76 |
Number of Pages | 8 |
Year | 2025 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 1541-1672 |
1941-1294 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/MIS.2024.3508432 |
Web Address (URL) | https://ieeexplore.ieee.org/document/10897267 |
Abstract | Aspect-based sentiment analysis (ABSA) involves identifying sentiment toward specific aspect terms in a sentence and allows us to uncover people's nuanced perspectives and attitudes on particular aspects of a product, service, or topic. However, the scarcity of labeled data poses a significant challenge to training high-quality models. To address this issue, we explore the potential of data augmentation using ChatGPT, a well-performing large language model, to enhance the sentiment classification performance toward aspect terms. Specifically, we explore three data augmentation strategies based on ChatGPT: context-focused, aspect-focused, and context-aspect data augmentation techniques. Context-focused data augmentation focuses on changing the word expression of context words in the sentence while keeping aspect terms unchanged. In contrast, aspect-focused data augmentation aims to change aspect terms but keep context words unchanged. Context-aspect data augmentation integrates these two data augmentations to generate augmented samples. Furthermore, we incorporate contrastive learning into the ABSA tasks to improve performance. Extensive experiments show that all three data augmentation techniques lead to performance improvements, with the context-aspect data augmentation strategy performing best and surpassing the performance of the baseline models. |
Keywords | ChatGPT |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461103. Deep learning |
Public Notes | The accessible file is the accepted version of the paper. Please refer to the URL for the published version. |
Byline Affiliations | Hong Kong Metropolitan University, China |
Lingnan University of Hong Kong, China | |
School of Mathematics, Physics and Computing |
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https://research.usq.edu.au/item/zx215/exploring-chatgpt-based-augmentation-strategies-for-contrastive-aspect-based-sentiment-analysis
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