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International Conference on Artificial Neural Networks

ICANN 2022: Artificial Neural Networks and Machine Learning – ICANN 2022 pp 270–281Cite as

MTHGAT: A Neural Multi-task Model for Aspect Category Detection and Aspect Term Sentiment Analysis on Restaurant Reviews

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13530))

Abstract

Aspect-based sentiment analysis (ABSA) is one of the fundamental task in text sentiment analysis which aims to analyze the sentiment polarity of a specific aspect (terms or categories) in a given sentence. Aspect category detection (ACD) and aspect term sentiment analysis (ATSA) are both sub-task of ABSA. However, most of the previous methods regard them as two separate tasks, and ignore the potential relationship between them. In this paper, we propose a multi-task hierarchical graph attention network (MTHGAT) which contains two levels of graph attention networks, a sentence level and a document level. The former is built based on the reshaped dependency parse tree, the latter is built to gather information between sentences in a document. Extensive experiments are conducted on the two restaurant datasets in SemEval-2015 and SemeEval-2016. The results show that our proposed model performs better than most baseline methods.

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References

  1. Bakshi, R.K., Kaur, N., Kaur, R., Kaur, G.: Opinion mining and sentiment analysis. In: 2016 3rd international conference on computing for sustainable global development (INDIACom), pp. 452–455. IEEE (2016)

    Google Scholar 

  2. Brun, C., Perez, J., Roux, C.: XRCE at SemEval-2016 task 5: feedbacked ensemble modeling on syntactico-semantic knowledge for aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pp. 277–281 (2016)

    Google Scholar 

  3. Chen, P., Sun, Z., Bing, L., Yang, W.: 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 (2017)

    Google Scholar 

  4. Chen, Y.: Convolutional neural network for sentence classification. Master’s thesis, University of Waterloo (2015)

    Google Scholar 

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

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

    Google Scholar 

  7. Dozat, T., Manning, C.D.: Deep biaffine attention for neural dependency parsing. arXiv preprint arXiv:1611.01734 (2016)

  8. Huang, B., Carley, K.M.: Parameterized convolutional neural networks for aspect level sentiment classification. arXiv preprint arXiv:1909.06276 (2019)

  9. Huang, B., Carley, K.M.: Syntax-aware aspect level sentiment classification with graph attention networks. arXiv preprint arXiv:1909.02606 (2019)

  10. Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds.) SBP-BRiMS 2018. LNCS, vol. 10899, pp. 197–206. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93372-6_22

    Chapter  Google Scholar 

  11. Khalil, T., El-Beltagy, S.R.: Niletmrg at semeval-2016 task 5: deep convolutional neural networks for aspect category and sentiment extraction. In: Proceedings of the 10th International Workshop on Semantic Evaluation (SEMEVAL-2016), pp. 271–276 (2016)

    Google Scholar 

  12. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

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

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

  15. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Comput. Linguist 35(2), 311–312 (2009)

    Google Scholar 

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

    Google Scholar 

  17. Sun, C., Huang, L., Qiu, X.: Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence. arXiv preprint arXiv:1903.09588 (2019)

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

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

  20. Wagner, J., et al.: DCU: aspect-based polarity classification for semeval task 4 (2014)

    Google Scholar 

  21. Wang, J., et al.: Aspect sentiment classification with both word-level and clause-level attention networks. In: IJCAI, vol. 2018, pp. 4439–4445 (2018)

    Google Scholar 

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

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

    Google Scholar 

  24. Xu, H., Liu, B., Shu, L., Yu, P.S.: Bert post-training for review reading comprehension and aspect-based sentiment analysis. arXiv preprint arXiv:1904.02232 (2019)

  25. Xue, W., Li, T.: Aspect based sentiment analysis with gated convolutional networks. arXiv preprint arXiv:1805.07043 (2018)

  26. Yin, C., Zhou, Q., Ge, L., Ou, J.: Multi-hop Syntactic Graph Convolutional Networks for Aspect-Based Sentiment Classification. Knowledge Science, Engineering and Management (2020)

    Google Scholar 

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

  28. Zhang, Y., Qi, P., Manning, C.D.: Graph convolution over pruned dependency trees improves relation extraction. arXiv preprint arXiv:1809.10185 (2018)

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, China

    Liang Ge & Jun Li

Authors
  1. Liang Ge
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  2. Jun Li
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Corresponding author

Correspondence to Jun Li .

Editor information

Editors and Affiliations

  1. University of the West of England, Bristol, UK

    Elias Pimenidis

  2. Lancaster University, Lancaster, UK

    Plamen Angelov

  3. Digital Innovation, Teeside University, Middlesbrough, UK

    Chrisina Jayne

  4. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  5. The University of the West of England, Bristol, UK

    Mehmet Aydin

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Cite this paper

Ge, L., Li, J. (2022). MTHGAT: A Neural Multi-task Model for Aspect Category Detection and Aspect Term Sentiment Analysis on Restaurant Reviews. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13530. Springer, Cham. https://doi.org/10.1007/978-3-031-15931-2_23

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  • DOI: https://doi.org/10.1007/978-3-031-15931-2_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15930-5

  • Online ISBN: 978-3-031-15931-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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