iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S11227-023-05745-8
Triple-channel graph attention network for improving aspect-level sentiment analysis | The Journal of Supercomputing Skip to main content
Springer Nature Link
Log in

Triple-channel graph attention network for improving aspect-level sentiment analysis

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Aspect-level sentiment classification is a fine-grained sentiment analysis that primarily focuses on predicting the sentiment polarity of aspects within a sentence. At present, many methods employ graph convolutional networks (GCN) to extract hidden semantic or syntactic information from sentences, achieving good results. However, these existing methods often overlook the relationships between multiple aspects within a sentence, treating aspects separately and thus neglecting the sentiment connections. To address this issue, this paper introduces a triple-channel graph attention network (TC-GAT) to capture semantics, syntax and multiple aspects dependencies information. In addition, a simple and effective fusion mechanism is proposed to comprehensively integrate these three types of information. Experiments are carried out on three commonly datasets, and the results verify the effectiveness of our proposed model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

References

  1. Jiang L, Yu M, Zhou M, Liu X, Zhao T (2011) Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp 151–160

  2. Weichselbraun A, Gindl S, Scharl A (2013) Extracting and grounding contextualized sentiment lexicons. IEEE Intell Syst 28(2):39–46

    Article  Google Scholar 

  3. Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp 231–240

  4. Gu S, Zhang L, Hou Y, Song Y (2018) A position-aware bidirectional attention network for aspect-level sentiment analysis. In: Proceedings of the 27th International Conference on Computational Linguistics, pp 774–784

  5. 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

  6. 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

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

  8. Huang L, Sun X, Li S, Zhang L, Wang H (2020) Syntax-aware graph attention network for aspect-level sentiment classification. In: Proceedings of the 28th International Conference on Computational Linguistics, pp 799–810

  9. Ke W, Gao J, Shen H, Cheng X (2021) Incorporating explicit syntactic dependency for aspect level sentiment classification. Neurocomputing 456:394–406

    Article  Google Scholar 

  10. Tran TT, Miwa M, Ananiadou S (2020) Syntactically-informed word representations from graph neural network. Neurocomputing 413:431–443

    Article  Google Scholar 

  11. Asada M, Miwa M, Sasaki Y (2017) Extracting drug-drug interactions with attention cnns. In BioNLP 2017:9–18

    Google Scholar 

  12. Asada M, Gunasekaran N, Miwa M, Sasaki Y (2021) Representing a heterogeneous pharmaceutical knowledge-graph with textual information. Front Res Metrics Anal 6:670206

    Article  Google Scholar 

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

  14. 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

  15. 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

  16. 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

  17. Tang D, Qin B, Feng X, Liu T (2015) Effective lstms for target-dependent sentiment classification. arXiv preprint arXiv:1512.01100

  18. Yang M, Tu W, Wang J, Xu F, Chen X (2017) Attention based lstm for target dependent sentiment classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 31

  19. Tian Y, Chen G, Song Y (2021) Aspect-based sentiment analysis with type-aware graph convolutional networks and layer ensemble. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 2910–2922

  20. 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

  21. Yu H, Lu G, Cai Q, Xue Y (2022) A kge based knowledge enhancing method for aspect-level sentiment classification. Mathematics 10(20):3908

    Article  Google Scholar 

  22. Zhao P, Hou L, Wu O (2020) Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification. Knowl Based Syst 193:105443

    Article  Google Scholar 

  23. Zeng J, Liu T, Jia W, Zhou J (2022) Relation construction for aspect-level sentiment classification. Inf Sci 586:209–223

    Article  Google Scholar 

  24. Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903

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

  26. Graves A, Mohamed AR, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp 6645–6649. IEEE

  27. 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

  28. Mrini K, Dernoncourt F, Bui T, Chang W, Nakashole N (2019) Rethinking self-attention: an interpretable selfattentive encoder-decoder parser. arXiv preprint arXiv:1911.03875

  29. Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, AL-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, De Clercq O, et al (2016) Semeval-2016 task 5: aspect based sentiment analysis. In: ProWorkshop on semantic evaluation (SemEval-2016), pp 19–30. Association for Computational Linguistics

  30. Kiritchenko S, Zhu X, Cherry C, Mohammad S (2014) Detecting aspects and sentiment in customer reviews. In: 8th International Workshop on Semantic Evaluation (SemEval), pp 437–442

  31. Mikolov TA (2012) Statistical language models based on neural networks

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

  33. Zhou J, Huang JX, Hu QV, He L (2020) Sk-gcn: modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowl Based Syst 205:106292

    Article  Google Scholar 

  34. 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

  35. 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

  36. Li P, Li P, Xiao X (2023) Aspect-pair supervised contrastive learning for aspect-based sentiment analysis. Knowl Based Syst 274:110648

    Article  Google Scholar 

  37. Liu H, Wu Y, Li Q, Lu W, Li X, Wei J, Liu X, Feng J (2023) Enhancing aspect-based sentiment analysis using a dual-gated graph convolutional network via contextual affective knowledge. Neurocomputing 553:126526

    Article  Google Scholar 

  38. 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 

  39. Li P, Li P, Xiao X (2023) Aspect-pair supervised contrastive learning for aspect-based sentiment analysis. Knowl Based Syst 274:110648

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronic Information, Wuhan University, Wuhan, 430072, China

    Chao Zhu, Benshun Yi & Laigan Luo

Authors
  1. Chao Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Benshun Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laigan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CZ provides the main innovation points, experiments and paper writing. BY offered some suggestions in the paper innovation. LL offered some advice and help during the experiment.

Corresponding author

Correspondence to Benshun Yi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interests to this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, C., Yi, B. & Luo, L. Triple-channel graph attention network for improving aspect-level sentiment analysis. J Supercomput 80, 7604–7623 (2024). https://doi.org/10.1007/s11227-023-05745-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05745-8

Keywords

Search

Navigation