Skip to main content
Springer Nature Link
Log in

Opinion Transmission Network for Jointly Improving Aspect-Oriented Opinion Words Extraction and Sentiment Classification

  • Conference paper
  • First Online:
Natural Language Processing and Chinese Computing (NLPCC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12430))

Abstract

Aspect-level sentiment classification (ALSC) and aspect oriented opinion words extraction (AOWE) are two highly relevant aspect-based sentiment analysis (ABSA) subtasks. They respectively aim to detect the sentiment polarity and extract the corresponding opinion words toward a given aspect in a sentence. Previous works separate them and focus on one of them by training neural models on small-scale labeled data, while neglecting the connections between them. In this paper, we propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE to achieve the goal of facilitating them simultaneously. Specifically, we design two tailor-made opinion transmission mechanisms to control opinion clues flow bidirectionally, respectively from ALSC to AOWE and AOWE to ALSC. Experiment results on two benchmark datasets show that our joint model outperforms strong baselines on the two tasks. Further analysis also validates the effectiveness of opinion transmission mechanisms.

C. Ying and Z. Wu—Authors Contributed Equally.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Chen, P., Sun, Z., Bing, L., Yang, W.: Recurrent attention network on memory for aspect sentiment analysis. In: EMNLP, pp. 452–461 (2017)

    Google Scholar 

  2. Chen, Z., Qian, T.: Transfer capsule network for aspect level sentiment classification. In: ACL, pp. 547–556 (2019)

    Google Scholar 

  3. Fan, Z., Wu, Z., Dai, X., Huang, S., Chen, J.: Target-oriented opinion words extraction with target-fused neural sequence labeling. In: NAACL, pp. 2509–2518 (2019)

    Google Scholar 

  4. Gu, S., Zhang, L., Hou, Y., Song, Y.: A position-aware bidirectional attention network for aspect-level sentiment analysis. In: COLING, pp. 774–784 (2018)

    Google Scholar 

  5. He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: Exploiting document knowledge for aspect-level sentiment classification. In: ACL, pp. 579–585, July 2018

    Google Scholar 

  6. Hu, M., Liu, B.: Mining and summarizing customer reviews. In: ACM SIGKDD, pp. 168–177 (2004)

    Google Scholar 

  7. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)

    Google Scholar 

  8. Ma, D., Li, S., Zhang, X., Wang, H.: Interactive attention networks for aspect-level sentiment classification. In: IJCAI, pp. 4068–4074 (2017)

    Google Scholar 

  9. Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)

    Google Scholar 

  10. Pontiki, M., et al.: SemEval-2016 task 5: aspect based sentiment analysis. In: (SemEval 2016) (2016)

    Google Scholar 

  11. Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis. In: (SemEval 2014), pp. 27–35, August 2014

    Google Scholar 

  12. Poria, S., Cambria, E., Gelbukh, A.: Aspect extraction for opinion mining with a deep convolutional neural network. Knowl.-Based Syst. 108, 42–49 (2016)

    Article  Google Scholar 

  13. Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. In: EMNLP, pp. 214–224, Association for Computational Linguistics, Austin, November 2016

    Google Scholar 

  14. Wang, C., Xu, B.: Convolutional neural network with word embeddings for Chinese word segmentation. In: IJCNLP, pp. 163–172, November 2017

    Google Scholar 

  15. Wang, Y., Huang, M., Zhu, X., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: EMNLP, pp. 606–615 (2016)

    Google Scholar 

  16. Wu, Z., Zhao, F., Dai, X., Huang, S., Chen, J.: Latent opinions transfer network for target-oriented opinion words extraction. In: AAAI, pp. 9298–9305 (2020)

    Google Scholar 

  17. Xue, W., Li, T.: Aspect based sentiment analysis with gated convolutional networks. In: ACL, pp. 2514–2523 (2018)

    Google Scholar 

  18. Yang, J., Yang, R., Wang, C., Xie, J.: Multi-entity aspect-based sentiment analysis with context, entity and aspect memory. In: AAAI (2018)

    Google Scholar 

  19. Zhu, P., Qian, T.: Enhanced aspect level sentiment classification with auxiliary memory. In: COLING, pp. 1077–1087 (2018)

    Google Scholar 

  20. Zhu, Y., Wang, G.: CAN-NER: convolutional attention network for Chinese named entity recognition. In: NAACL, pp. 3384–3393 (2019)

    Google Scholar 

  21. Zhuang, L., Jing, F., Zhu, X.Y.: Movie review mining and summarization. In: CIKM, pp. 43–50 (2006)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the NSFC (No. 61976114, 61936012) and National Key R&D Program of China (No. 2018YFB1005102).

Author information

Authors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Chengcan Ying, Zhen Wu, Xinyu Dai, Shujian Huang & Jiajun Chen

Authors
  1. Chengcan Ying
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinyu Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shujian Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiajun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinyu Dai .

Editor information

Editors and Affiliations

  1. ECE & Ingenuity Labs Research Institute, Queen’s University, Kingston, ON, Canada

    Xiaodan Zhu

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Min Zhang

  3. School of Computer Science and Technology, Soochow University, Suzhou, China

    Yu Hong

  4. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Ruifang He

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ying, C., Wu, Z., Dai, X., Huang, S., Chen, J. (2020). Opinion Transmission Network for Jointly Improving Aspect-Oriented Opinion Words Extraction and Sentiment Classification. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12430. Springer, Cham. https://doi.org/10.1007/978-3-030-60450-9_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60450-9_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60449-3

  • Online ISBN: 978-3-030-60450-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships