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Multi-target opinion words extraction

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Abstract

Target-oriented Opinion Words Extraction (TOWE) emerges as a novel subtask of Aspect-Based Sentiment Analysis (ABSA), exerting profound impacts on numerous downstream tasks and real-world applications. Current research on target-oriented opinion words extraction focuses solely on extracting opinions for a single target. However, when handling multiple “target-opinion” pairs within a single data instance, inefficiency and time-consuming issues arise. Therefore, we propose an alternative solution to target-oriented opinion words extraction, termed Multi-Target Opinion Words Extraction (MTOWE). To achieve multi-target opinion words extraction, we conduct a statistical analysis and reconstruction of popular datasets(14lap, 14res, 15res, and 16res) in the target-oriented opinion words extraction task, denoted as M-14lap, M-14res, M-15res, and M-16res, respectively. We propose a Graph-based Collaborative Understanding Network for MTOWE. Experimental results demonstrate that the graph-based collaborative understanding network significantly outperforms the baseline models on the four datasets of the multi-target opinion words extraction task, achieving F1 scores of 74.04, 82.30, 74.92, and 83.49, respectively. More importantly, when processing the same batch of information, the inference time of MTOWE is significantly reduced. On the four datasets, the average inference time of MTOWE is only 70% of that of TOWE, while the F1 scores of MTOWE are only slightly lower than those of TOWE.

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Data Availability

The code and data are available from the corresponding author on reasonable request.

References

  1. Fan Z, Wu Z, Dai X, Huang S, Chen J (2019) Target-oriented opinion words extraction with target-fused neural sequence labeling. In: Burstein J, Doran C, Solorio T, (Eds), Proceedings of the 2019 Conference of the North American chapter of the association for computational linguistics: human language technologies vol 1, pp 2509–2518, Minneapolis, Minnesota. Association for Computational Linguistics

  2. Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, AL-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, De Clercq O, Hoste V, Apidianaki M, Tannier X, Loukachevitch N, Kotelnikov E, Bel N, Jiménez-Zafra SM, Eryiğit G (2016) SemEval-2016 task 5: Aspect based sentiment analysis. In: Bethard S, Carpuat M, Cer D, Jurgens D, Nakov P, Zesch T (Eds) Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp 19–30, San Diego, California. Association for Computational Linguistics

  3. Pontiki M, Galanis D, Papageorgiou H, Manandhar S, Androutsopoulos I (2015) SemEval-2015 task 12: Aspect based sentiment analysis. In: Nakov P, Zesch T, Cer D, Jurgens D (Eds) Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), pp 486–495, Denver, Colorado. Association for Computational Linguistics

  4. Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, AL-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, De Clercq O, Hoste V, Apidianaki M, Tannier X, Loukachevitch N, Kotelnikov E, Bel N, Jiménez-Zafra SM, Eryiğit G (2016) SemEval-2016 task 5: Aspect based sentiment analysis. In: Bethard S, Carpuat M, Cer D, Jurgens D, Nakov P, Zesch T (Eds) Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 19–30, San Diego, California. Association for Computational Linguistics

  5. Peng H, Xu L, Bing L, Huang F, Lu W, Si L (2020) Knowing what, how and why: a near complete solution for aspect-based sentiment analysis. In: Proceedings of the AAAI conference on artificial intelligence vol 34, pp 8600–8607

  6. Cai H, Xia R, Yu J (2021) Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions. 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 340–350

  7. Wu Z, Zhao F, Dai XY, Huang S, Chen J (2020) Latent opinions transfer network for target-oriented opinion words extraction. In: Proceedings of the AAAI conference on artificial intelligence vol 34, pp 9298–9305

  8. Cheng Zifeng, Jiang Zhiwei, Yin Yafeng, Li Na, Qing Gu (2021) A unified target-oriented sequence-to-sequence model for emotion-cause pair extraction. IEEE/ACM Trans Audio Speech Lang Process 29:2779–2791

    Article  MATH  Google Scholar 

  9. Wang H, Qiu X, Tan X (2024) Multivariate graph neural networks on enhancing syntactic and semantic for aspect-based sentiment analysis. Appl Intell 1–18

  10. Wang Z, Li Q, Wang B, Tong W, Chang C (2024) Improving text classification through pre-attention mechanism-derived lexicons. Appl Intell 1:1–14

    MATH  Google Scholar 

  11. Mensah S, Sun K, Aletras N (2021) An empirical study on leveraging position embeddings for target-oriented opinion words extraction. In: Moens M-F, Huang X, Specia L, Yih SW-t (Eds) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp 9174–9179, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics

  12. Jiang J, Wang A, Aizawa A (2021) Attention-based relational graph convolutional network for target-oriented opinion words extraction. In: Proceedings of the 16th conference of the european chapter of the association for computational linguistics: main volume, pp 1986–1997

  13. Zhang J, Li F, Zhang Z, Xu G, Wang Y, Wang X, Zhang Y (2021) Integrate syntax information for target-oriented opinion words extraction with target-specific graph convolutional network. Neurocomputing 440:321–335

    Article  MATH  Google Scholar 

  14. Liu P, Joty S, Meng H (2015) Fine-grained opinion mining with recurrent neural networks and word embeddings. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1433–1443

  15. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. Toulon, France. Citation networks;Convolutional networks;First-order approximations;Graph structured data;Hidden layers;Knowledge graphs;Scalable approach;Semi-supervised classification;

  16. Dai Y, Wang P, Zhu X (2022) Reasoning over multiplex heterogeneous graph for target-oriented opinion words extraction. Knowl Based Syst 236:107723

    Article  MATH  Google Scholar 

  17. Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

  18. Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (Eds) Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (Long and Short Papers), pp 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics

  19. Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877–1901

    Google Scholar 

  20. Jiang J, Wang A, Aizawa A (2021) Attention-based relational graph convolutional network for target-oriented opinion words extraction. In: Proceedings of the 16th conference of the European chapter of the association for computational linguistics: main volume, pp 1986–1997

  21. Schlichtkrull M, Kipf TN, Bloem P, Van Den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: The semantic web: 15th international conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, proceedings 15, pp 593–607. Springer

  22. Dwivedi VP, Joshi CK, Luu AT, Laurent T, Bengio Y, Bresson X (2023) Benchmarking graph neural networks. J Mach Learn Res 24(43):1–48

    MathSciNet  MATH  Google Scholar 

  23. Tailor SA, Opolka FL, Lio P, Lane ND (2021) Do we need anisotropic graph neural networks? In: International conference on learning representations

  24. Velikovi P, Casanova A, Lio P, Cucurull G, Romero A, Bengio Y (2018) Graph attention networks. Vancouver, BC, Canada. Citation networks;Graph neural networks;Graph structured data;Matrix operations;Novel neural network;Protein-protein interactions;Stacking layers;State of the art;

  25. Honnibal M, Johnson M (2015) An improved non-monotonic transition system for dependency parsing. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 1373–1378

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Contributions

Zixue Zhao: Conceptualization, Methodology, Writing-original draft, Jiangsheng Wu: Supervision, Writing-review. Shuaibo Li: Data curation, Methodology, Zhengpeng Li: Visualization, Investigation, Kejin Li: Methodology, Investigation.

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Correspondence to Jiansheng Wu.

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Zhao, Z., Li, S., Li, Z. et al. Multi-target opinion words extraction. Appl Intell 55, 49 (2025). https://doi.org/10.1007/s10489-024-05871-7

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