Abstract
Structured Sentiment Analysis (SSA) aims to extract the complete sentiment structure from a given text. Existing approaches predominantly rely on the interactions of words to predict the relationships between sentiment elements. While these methods have shown effectiveness, they overlook the rich label semantics associated with SSA tasks and necessitate extensive task-specific designs. In order to address the above problems, we propose a generative framework for tackling the SSA task. We designed two templates to transform the SSA task into a text generation problem, which facilitate the training process by formulating the SSA task as a text generation problem. Through experiments conducted on three SSA datasets, we demonstrate that our proposed generative approach outperforms all existing methods, thereby highlighting the advantages of employing the generative model for SSA.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agerri, R., Cuadros, M., Gaines, S., Rigau, G.: OpeNER: open polarity enhanced named entity recognition. In: Sociedad Española para el Procesamiento del Lenguaje Natural, vol. 51, pp. 215–218 (2013)
Athiwaratkun, B., dos Santos, C., Krone, J., Xiang, B.: Augmented natural language for generative sequence labeling. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 375–385 (2020)
Barnes, J., Kurtz, R., Oepen, S., Øvrelid, L., Velldal, E.: Structured sentiment analysis as dependency graph parsing. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 3387–3402 (2021)
Katiyar, A., Cardie, C.: Investigating LSTMS for joint extraction of opinion entities and relations. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 919–929 (2016)
Liu, X., et al.: GPT understands, too. arXiv preprint arXiv:2103.10385 (2021)
Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)
Paolini, G., et al.: Structured prediction as translation between augmented natural languages. In: International Conference on Learning Representations (2020)
Peng, L., Li, Z., Zhao, H.: Sparse fuzzy attention for structured sentiment analysis. arXiv preprint arXiv:2109.06719 (2021)
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1), 5485–5551 (2020)
Toprak, C., Jakob, N., Gurevych, I.: Sentence and expression level annotation of opinions in user-generated discourse. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 575–584 (2010)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Lang. Resour. Eval. 39(2–3), 165–210 (2005)
Wu, Z., Ying, C., Zhao, F., Fan, Z., Dai, X., Xia, R.: Grid tagging scheme for aspect-oriented fine-grained opinion extraction. In: Findings of the Association for Computational Linguistics, EMNLP 2020, pp. 2576–2585 (2020)
Xia, Q., et al.: A unified span-based approach for opinion mining with syntactic constituents. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1795–1804 (2021)
Xu, Q., Li, B., Li, F., Fu, G., Ji, D.: A dual-pointer guided transition system for end-to-end structured sentiment analysis with global graph reasoning. Inf. Process. Manage. 59(4), 102992 (2022)
Zhang, W., Deng, Y., Li, X., Yuan, Y., Bing, L., Lam, W.: Aspect sentiment quad prediction as paraphrase generation. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 9209–9219 (2021)
Acknowledgement
This research was supported in part by the National Natural Science Foundation of China (62006062, 62176076), Natural Science Foundation of Guangdong (2023A1515012922), and Key Technologies Research and Development Program of Shenzhen JSGG20210802154400001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, Y., Zhang, Y., Yang, Y., Xu, R. (2023). A Generative Model for Structured Sentiment Analysis. In: Yang, Y., Wang, X., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2023 . AIMS 2023. Lecture Notes in Computer Science, vol 14202. Springer, Cham. https://doi.org/10.1007/978-3-031-45140-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-45140-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45139-3
Online ISBN: 978-3-031-45140-9
eBook Packages: Computer ScienceComputer Science (R0)