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An aspect-opinion joint extraction model for target-oriented opinion words extraction on global space

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Abstract

In aspect-based sentiment analysis, target-oriented opinion words extraction (TOWE) aims to extract opinion words based on aspect terms. Most current methods used in TOWE tasks only focus on explicit aspects and tend to overlook the implicit aspects, leading to a bias in the sample selection process and incomplete modeling of the TOWE tasks. Therefore, it is essential to consider both explicit and implicit aspects simultaneously in the modeling process of TOWE tasks. This paper proposes an aspect-opinion joint extraction (AOJE) model composed of an aspect term extraction unit (ATEU) and a target-oriented opinion words extraction unit (TOWEU). ATEU first is responsible for extracting aspect terms and converting them into prompt templates. TOWEU uses these templates to obtain opinion words for specific targets. This model is trained and evaluated on global space, including explicit and implicit aspects. This approach effectively addresses the issue of sample selection bias. The proposed AOJE method performs better than existing methods by an average of 4.06% on the Macro-F1 score on the SemEval 14-16 datasets. In particular, the AOJE model shows significant improvements compared to the IOG (Inward-Outward LSTM+Global context) model, with Macro-F1 scores increasing by 9.00%, 8.48%, 7.41%, and 9.21% on the Laptop 14, Restaurant 14, Restaurant 15, and Restaurant 16 datasets, respectively. These experimental results indicate that the AOJE model trained on global space significantly enhances the performance of TOWE and improves generalization capabilities.

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Data Availability

The datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors thank the reviewers’ and editors’ helpful comments and valuable suggestions, which have improved the quality of our manuscript. This work is partially supported by the Sichuan Science and Technology Program (Nos. 2022YFG0378, 2023YFS0424, 2023YFQ0044, and 2023YFH0058), Yibin Science and Technology Program (2023SF004), and the Engineering Research Center for ICH Digitalization and Multi-source Information Fusion (Fujian Polytechnic Normal University), Fujian Province University (G3-KF2022).

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Contributions

Jiamin Huang: Methodology, Software, Data curation, Writing-original, Writing-review & editing, draft. Xianyong Li: Supervision, Writing-review & editing, Funding acquisition, Formal analysis. Yajun Du: Funding acquisition, Investigation, Validation. Yongquan Fan: Formal analysis, Software. Dong Huang: Formal analysis, Software. Xiaoliang Chen: Funding acquisition, Formal analysis. All authors contributed to the manuscript revision and read and approved the submitted version.

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Correspondence to Xianyong Li.

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All sources of funding are our research projects. There are no potential conflicts of interest. Human Participants and Animals do not involve in this research. Informed consent for data used has been included in this study.

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Appendix

Tables 10 and 11 list the descriptions of abbreviations and definitions of symbols in this paper.

Table 10 Desciptions of abbreviations
Table 11 Definitions of symbols

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Huang, J., Li, X., Du, Y. et al. An aspect-opinion joint extraction model for target-oriented opinion words extraction on global space. Appl Intell 55, 23 (2025). https://doi.org/10.1007/s10489-024-05865-5

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