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Aspect-level sentiment classification with aspect-opinion sentence pattern connection graph convolutional networks

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Abstract

Attempting to identify and determine the sentiment polarity of one or more aspects (i.e., aspect words) in a sentence, Aspect-Level Sentiment Classification (abbreviated as ALSC) is a fine-grained sentiment classification task. Graph convolutional networks on dependency trees are now widely being used in related research to improve the accuracy of ALSC. The key to determining the polarity of aspectual emotions is to find the opinion, i.e., the opinion word, that is, most relevant to the aspectual emotion. However, in the dependency tree, a significant portion of aspect words and opinion words are not directly connected. And long-distance connections can lead to the model not paying enough attention to opinion words and losing information. In order to address this issue, by examining dependency syntactic structure and syntactic knowledge, we propose Aspect-opinion Sentence pattern Connection (ASC) to strengthen sentiment dependency graphs. We then develop the ASC-GCN to efficiently use the strengthened dependencies. Experimental results on four public benchmark datasets indicate that our approach achieves excellent performance on a lightweight model.

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Data availability

The datasets used in this work are publicly available on GitHub https://github.com/lylhy/RLRR.

Notes

  1. The GCN layer in this work is inspired by previous outstanding word [7].

  2. We use spaCy toolkit: https://spacy.io/.

References

  1. Chen C, Teng Z, Zhang Y (2020) Inducing target-specific latent structures for aspect sentiment classification. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 5596–5607

  2. Jiang B, Xu G, Liu P (2023) Aspect-level sentiment classification via location enhanced aspect-merged graph convolutional networks. J Supercomput 79(9):9666

    Article  Google Scholar 

  3. Li X, Bing L, Lam W, Shi B (2018) Transformation networks for target-oriented sentiment classification. arXiv preprint arXiv:1805.01086

  4. Song Y, Wang J, Jiang T, Liu Z, Rao Y (2019) Attentional encoder network for targeted sentiment classification. arXiv preprint arXiv:1902.09314

  5. Wang Y, Huang M, Zhu X, Zhao, L (2016) Attention-based lstm for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp 606–615

  6. Sun K, Zhang R, Mensah S, Mao Y, Liu X (2019) Aspect-level sentiment analysis via convolution over dependency tree. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp 5679–5688

  7. Zhang C, Li Q, Song D (2019) Aspect-based sentiment classification with aspect-specific graph convolutional networks. arXiv preprint arXiv:1909.03477

  8. Zhang M, Qian T (2020) Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 3540–3549

  9. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907

  10. Cambria E, Liu Q, Decherchi S, Xing F, Kwok K (2022) Senticnet 7: a commonsense-based neurosymbolic ai framework for explainable sentiment analysis. Proceedings of LREC 2022

  11. Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)

  12. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inform Process Syst 30:17

    Google Scholar 

  13. Chen P, Sun Z, Bing L, Yang W (2017) Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 452–461

  14. Fan F, Feng Y, Zhao D (2018) Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3433–3442

  15. Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

  16. Kumar A, Gupta P, Balan R, Neti LBM, Malapati A (2021) Bert based semi-supervised hybrid approach for aspect and sentiment classification. Neural Process Lett 53:4207–4224

    Article  Google Scholar 

  17. Peng Y, Xiao T, Yuan H (2022) Cooperative gating network based on a single bert encoder for aspect term sentiment analysis. Appl Intell 52(5):5867–5879

    Article  Google Scholar 

  18. Wang K, Shen W, Yang Y, Quan X, Wang R (2020) Relational graph attention network for aspect-based sentiment analysis. arXiv preprint arXiv:2004.12362

  19. Li R, Chen H, Feng F, Ma Z, Wang X, Hovy E (2021) Dual graph convolutional networks for aspect-based sentiment analysis. 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. 6319–6329

  20. Mihaylov T, Frank A (2018) Knowledgeable reader: enhancing cloze-style reading comprehension with external commonsense knowledge. arXiv preprint arXiv:1805.07858

  21. Chaturvedi I, Satapathy R, Cavallari S, Cambria E (2019) Fuzzy commonsense reasoning for multimodal sentiment analysis. Patt Recognit Lett 125:264–270

    Article  Google Scholar 

  22. Cai Y, Ke W, Cui E, Yu F (2022) A deep recommendation model of cross-grained sentiments of user reviews and ratings. Inform Process Manag 59(2):102842

    Article  Google Scholar 

  23. Zhu P, Hu J, Zhang Y, Li X (2021) Enhancing traceability of infectious diseases: a blockchain-based approach. Inform Process Manag 58(4):102570

    Article  Google Scholar 

  24. Zhu P, Hu J, Li X, Zhu Q (2023) Using blockchain technology to enhance the traceability of original achievements. IEEE Trans Eng Manag 70(5):1693–1707

    Article  Google Scholar 

  25. Zhu P, Miao C, Wang Z, Li X (2023) Informational cascade, regulatory focus and purchase intention in online flash shopping. Electron Comm Res Appl 62:101343

    Article  Google Scholar 

  26. Zhu P, Zhang H, Shi Y, Xie W, Pang M, Shi Y (2024) A novel discrete conformable fractional grey system model for forecasting carbon dioxide emissions. Environ Develop Sustain 2024:1–29

    Google Scholar 

  27. Cambria E, Havasi C, Hussain A (2012) Senticnet 2: a semantic and affective resource for opinion mining and sentiment analysis. In: Twenty-Fifth International FLAIRS Conference

  28. Cambria E, Hussain A, Havasi C, Eckl C (2009) Common sense computing: From the society of mind to digital intuition and beyond. In: European Workshop on Biometrics and Identity Management, pp. 252–259 . Springer

  29. Cambria E, Li Y, Xing FZ, Poria S, Kwok K (2020) Senticnet 6: Ensemble application of symbolic and subsymbolic ai for sentiment analysis. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp 105–114

  30. Cambria E, Olsher D, Rajagopal D (2014) Senticnet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Twenty-eighth AAAI Conference on Artificial Intelligence

  31. Cambria E, Poria S, Bajpai R, Schuller B (2016) Senticnet 4: A semantic resource for sentiment analysis based on conceptual primitives. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2666–2677

  32. Cambria E, Poria S, Hazarika D, Kwok K (2018) Senticnet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 32

  33. Liang B, Su H, Gui L, Cambria E, Xu R (2022) Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl Based Syst 235:107643

    Article  Google Scholar 

  34. Gu T, Zhao H, He Z, Li M, Ying D (2023) Integrating external knowledge into aspect-based sentiment analysis using graph neural network. Knowl Based Syst 259:110025

    Article  Google Scholar 

  35. 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

  36. Xiao L, Xue Y, Wang H, Hu X, Gu D, Zhu Y (2022) Exploring fine-grained syntactic information for aspect-based sentiment classification with dual graph neural networks. Neurocomputing 471:48–59

    Article  Google Scholar 

  37. Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) SemEval-2014 task 4: Aspect Based Sentiment Analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (semeval 2014), pp. 27–35. Association for computational linguistics, Dublin, Ireland . https://doi.org/10.3115/v1/S14-2004

  38. Pontiki M, Galanis D, Papageorgiou H, Manandhar S, Androutsopoulos I (2015) Semeval-2015 task 12: Aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 486–495

  39. Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, AL-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, De Clercq O (2016) Semeval-2016 task 5: aspect based sentiment analysis. In: ProWorkshop on Semantic Evaluation (SemEval-2016), pp. 19–30 . Association for computational linguistics

  40. Ma D, Li S, Zhang X, Wang H (2017) Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893

  41. Zhang K, Zhang K, Zhang M, Zhao H, Liu Q, Wu W, Chen E (2022) Incorporating dynamic semantics into pre-trained language model for aspect-based sentiment analysis. arXiv preprint arXiv:2203.16369

  42. Liang B, Yin R, Gui L, Du J, Xu R (2020) Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 150–161

  43. Tang H, Ji D, Li C, Zhou Q (2020) Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: Proceedings of the 58th Annual Meeting of the Association for Computational linguistics, pp. 6578–6588

  44. Rogers A, Boyd-Graber JL, Okazaki N (eds.) (2023) Proceedings of the 61st Annual Meeting of the Association for Computational linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023. Association for computational linguistics, ??? . https://aclanthology.org/volumes/2023.acl-long/

  45. Zhang Z, Zhou Z, Wang Y (2022) Ssegcn: Syntactic and semantic enhanced graph convolutional network for aspect-based sentiment analysis. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4916–4925

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Funding

Key R & D project of Shandong Province, 2019JZZY010129. Shandong Provincial Social Science Planning Project under Award 19BJCJ51, Award 18CXWJ01, and Award 18BJYJ04. National Science Foundation of China (NSFC), 62006107. Natural Science Foundation of Shandong Province (Nos. ZR2020MF029 and ZR2020MF058).

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Author notes

  1. Hongye Li, Fuyong Xu and Zhiyu Zhang have contributed equally to this work.

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250300, China

    Hongye Li, Fuyong Xu & Peiyu Liu

  2. School of Information Science and Engineering, Linyi University, Linyi, 276000, China

    Wenyin Zhang

  3. Business School, University of Nottingham, Nottingham, UK

    Zhiyu Zhang

Authors
  1. Hongye Li
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  2. Fuyong Xu
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Contributions

Hongye Li helped in conceptualization, methodology, software, validation, and writing—original draft. Fuyong Xu contributed to writing—review & editing, and formal analysis. Zhiyu Zhang contributed to writing—review & editing and data curation. Wenyin Zhang helped in funding acquisition and project administration. Peiyu Liu worked in project administration and supervision.

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Correspondence to Peiyu Liu or Wenyin Zhang.

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Li, H., Xu, F., Zhang, Z. et al. Aspect-level sentiment classification with aspect-opinion sentence pattern connection graph convolutional networks. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06093-x

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