Skip to main content
SpringerLink
Log in

Linguistic Knowledge Based on Attention Neural Network for Targeted Sentiment Classification

  • Conference paper
  • First Online:
Book cover Chinese Lexical Semantics (CLSW 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11831))

Included in the following conference series:

Abstract

Deep learning approaches for targeted sentiment classification do not fully exploit linguistic knowledge. In this paper, we propose a Linguistic Knowledge based on Attention Neural Network (LKAN) to employ linguistic knowledge (e.g. sentiment lexicon, negation words, intensity words) to benefit targeted sentiment classification. Firstly, we extract linguistic knowledge words (e.g. sentiment lexicon, negation words, intensity words) in sentences by HowNet vocabulary. Then, we design an attention mechanism which drives the model to concentrate on such words and get a weighted combination of word embeddings as the final representation for the sentences. We evaluate our proposed approach on SemEval 2014 Task 4, whose performance as shown reaches the most advanced level.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.keenage.com.

References

  1. Kiritchenko, S., Zhu, X., Cherry, C., et al.: NRC-Canada-2014: detecting aspects and sentiment in customer reviews. In: Proceedings of SemEval, pp. 437–442 (2014)

    Google Scholar 

  2. Wagner, J., Arora, P., Cortes, S., et al.: DCU: aspect-based polarity classification for SemEval task 4. In: Proceedings of SemEval, pp. 223–229 (2016)

    Google Scholar 

  3. Vo, D.-T., Zhang, Y.: Target-dependent twitter sentiment classification with rich automatic features. In: Proceedings of IJCAI, pp. 1347–1353 (2015)

    Google Scholar 

  4. Socher, R., Pennington, J., Huang, E.H., et al.: Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 151–161 (2011)

    Google Scholar 

  5. Socher, R., Perelygin, A., Wu, J., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (2013)

    Google Scholar 

  6. Tai, K.S., Socher, R., et al.: Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of ACL-2015, pp. 1556–1566 (2015)

    Google Scholar 

  7. Tang, D., Qin, B., et al.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of COLING-2016, pp. 3298–3307 (2016)

    Google Scholar 

  8. Wang, Y., Huang, M., et al.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of EMNLP-2016, pp. 606–615 (2016)

    Google Scholar 

  9. Tang, D., Qin, B., et al.: Aspect level sentiment classification with deep memory network. In: Proceedings of EMNLP-2016, pp. 214–224 (2016)

    Google Scholar 

  10. Ma, D., Li, S., Zhang, X., et al.: Interactive attention networks for aspect-level sentiment classification. In: Proceedings of IJCAI-2017, pp. 4068–4074 (2017)

    Google Scholar 

  11. Li, L., Liu, Y., Zhou, A.Q.: Hierarchical attention based position-aware network for aspect-level sentiment analysis. In: Proceedings of the 22nd Conference on Computational Natural Language Learning, pp. 181–189 (2018)

    Google Scholar 

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

    Google Scholar 

  13. 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 

  14. Jiang, L., Yu, M., Zhou, M., et al.: Target-dependent Twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 151–160 (2011)

    Google Scholar 

  15. Zhao, W.X., Jiang, J., Yan, H., et al.: Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP 2010, pp. 56–65 (2010)

    Google Scholar 

  16. Chen, P., Sun, Z., Bing, L., et al.: 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 

  17. Zheng, S., Xia, R.: Left-center-right separated neural network for aspect-based sentiment analysis with rotatory attention. arxiv preprint arXiv:1802.00892 (2018)

  18. Huang, B., Ou, Y., Carley, K.M.: Aspect level sentiment classification with attention-over-attention neural networks. arxiv preprint arXiv:1804.06536 (2018)

  19. Qian, Q., Huang, M., Lei, J., et al.: Linguistically regularized LSTMs for sentiment classification. arXiv preprint arXiv:1611.03949 (2016)

  20. Kumar, A., Kawahara, D., Kurohashi, S.: Knowledge-enriched two-layered attention network for sentiment analysis. arXiv preprint arXiv:1805.07819 (2018)

  21. Lei, Z., Yang, Y., Yang, M., et al.: A multi-sentiment-resource enhanced attention network for sentiment classification. arXiv preprint arXiv:1807.04990 (2018)

  22. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  23. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

    Google Scholar 

  24. Chung, J., Gulcehre, C., Cho, K.H., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)

  25. Wagner, J., Arora, P., Cortes, S., et al.: DCU: aspect-based polarity classification for SemEval task 4. In: Proceedings of SemEval, pp. 223–229 (2014)

    Google Scholar 

  26. Tieleman, T., Hinton, G.: Lecture 6.5-RMSprop. In: COURSERA: Neural Networks for Machine Learning (2012)

    Google Scholar 

Download references

Acknowledgments

This work is supported by Beijing Natural Science Foundation (Project No. 4192057).

Author information

Authors and Affiliations

  1. Beijing Language and Culture University, Beijing, China

    Chengyu Du & Pengyuan Liu

Authors
  1. Chengyu Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pengyuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pengyuan Liu .

Editor information

Editors and Affiliations

  1. National Taiwan Normal University, Taipei, Taiwan

    Jia-Fei Hong

  2. Beijing Information Science and Technology University, Beijing, China

    Yangsen Zhang

  3. Beijing Language and Culture University, Beijing, China

    Pengyuan Liu

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

Du, C., Liu, P. (2020). Linguistic Knowledge Based on Attention Neural Network for Targeted Sentiment Classification. In: Hong, JF., Zhang, Y., Liu, P. (eds) Chinese Lexical Semantics. CLSW 2019. Lecture Notes in Computer Science(), vol 11831. Springer, Cham. https://doi.org/10.1007/978-3-030-38189-9_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-38189-9_50

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38188-2

  • Online ISBN: 978-3-030-38189-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation