Skip to main content
SpringerLink
Log in
Book cover

CCF International Conference on Natural Language Processing and Chinese Computing

NLPCC 2020: Natural Language Processing and Chinese Computing pp 777–788Cite as

Clue Extraction for Fine-Grained Emotion Analysis

  • Conference paper
  • First Online:

  • 3057 Accesses

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

Abstract

The emotion analysis in texts is a popular task in natural language processing. Existing research mainly recognizes the types of emotions by encoding sentences. However, in many cases, pure explicit encoding is easy to lose the fine-grained emotional clues hidden in the sentences due to the complexity and subtlety of emotions. We argue that narratives are inextricably emotionally structured and narrative analysis is used as a method to study and examine emotional clues. In this paper, we propose a new unified task: clue extraction for fine-grained emotion analysis (CLUE), which attempts to extract fine-grained emotional clue triples (Why, How, What): Why emotions occur, How people express emotions and What emotions they trigger. We propose a span-based method to address this CLUE task, which directly takes all possible spans as input. The advantage of span is to ensure that each clue is not segmented and semantically complete. The experimental results on a benchmark emotional cause corpus prove the feasibility of the CLUE task as well as the effectiveness of our method.

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.hitsz-hlt.com/?page_id=694.

  2. 2.

    https://github.com/huggingface/transformers.

References

  1. Abdul-Mageed, M., Ungar, L.: Emonet: fine-grained emotion detection with gated recurrent neural networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (volume 1: Long papers), pp. 718–728 (2017)

    Google Scholar 

  2. Chen, Y., Lee, S.Y.M., Li, S., Huang, C.R.: Emotion cause detection with linguistic constructions. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 179–187. Association for Computational Linguistics (2010)

    Google Scholar 

  3. Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on Machine learning, pp. 160–167 (2008)

    Google Scholar 

  4. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(Aug), 2493–2537 (2011)

    MATH  Google Scholar 

  5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186 (2019)

    Google Scholar 

  6. Ghazi, D., Inkpen, D., Szpakowicz, S.: Detecting emotion stimuli in emotion-bearing sentences. In: Gelbukh, A. (ed.) CICLing 2015. LNCS, vol. 9042, pp. 152–165. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18117-2_12

    Chapter  Google Scholar 

  7. Gui, L., Wu, D., Xu, R., Lu, Q., Zhou, Y.: Event-driven emotion cause extraction with corpus construction. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1639–1649 (2016)

    Google Scholar 

  8. Gui, L., Xu, R., Lu, Q., Wu, D., Zhou, Yu.: Emotion cause extraction, a challenging task with corpus construction. In: Li, Y., Xiang, G., Lin, H., Wang, M. (eds.) SMP 2016. CCIS, vol. 669, pp. 98–109. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-2993-6_8

    Chapter  Google Scholar 

  9. Gui, L., Yuan, L., Xu, R., Liu, B., Lu, Q., Zhou, Yu.: Emotion cause detection with linguistic construction in chinese weibo text. In: Zong, C., Nie, J.-Y., Zhao, D., Feng, Y. (eds.) NLPCC 2014. CCIS, vol. 496, pp. 457–464. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45924-9_42

    Chapter  Google Scholar 

  10. Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 328–339 (2018)

    Google Scholar 

  11. Khanpour, H., Caragea, C.: Fine-grained emotion detection in health-related online posts. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 1160–1166 (2018)

    Google Scholar 

  12. Kleres, J.: Emotions and narrative analysis: a methodological approach. J. Theory Soc. Behav. 41(2), 182–202 (2011)

    Article  Google Scholar 

  13. Lee, S.Y.M., Chen, Y., Huang, C.R.: A text-driven rule-based system for emotion cause detection. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 45–53. Association for Computational Linguistics (2010)

    Google Scholar 

  14. Li, X., Bing, L., Lam, W., Shi, B.: Transformation networks for target-oriented sentiment classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 946–956 (2018)

    Google Scholar 

  15. Liew, J.S.Y., Turtle, H.R.: Exploring fine-grained emotion detection in tweets. In: Proceedings of the NAACL Student Research Workshop, pp. 73–80 (2016)

    Google Scholar 

  16. Liew, J.S.Y., Turtle, H.R., Liddy, E.D.: Emotweet-28: a fine-grained emotion corpus for sentiment analysis. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pp. 1149–1156 (2016)

    Google Scholar 

  17. McCann, B., Bradbury, J., Xiong, C., Socher, R.: Learned in translation: contextualized word vectors. In: Advances in Neural Information Processing Systems, pp. 6294–6305 (2017)

    Google Scholar 

  18. Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 2227–2237 (2018)

    Google Scholar 

  19. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018). https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf

  20. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. 1(8), 9 (2019). https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

  21. Sarbin, T.R.: Emotions as narrative emplotments. In: Entering the Circle: Hermeneutic Investigation in Psychology, pp. 185–201 (1989)

    Google Scholar 

  22. Schmitt, M., Steinheber, S., Schreiber, K., Roth, B.: Joint aspect and polarity classification for aspect-based sentiment analysis with end-to-end neural networks. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 1109–1114 (2018)

    Google Scholar 

  23. Schuff, H., Barnes, J., Mohme, J., Padó, S., Klinger, R.: Annotation, modelling and analysis of fine-grained emotions on a stance and sentiment detection corpus. In: Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 13–23 (2017)

    Google Scholar 

  24. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  25. Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Recursive neural conditional random fields for aspect-based sentiment analysis. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 616–626 (2016)

    Google Scholar 

  26. Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  27. Wierzbicka, A.: Emotions Across Languages and Cultures: Diversity and Universals. Cambridge University Press, Cambridge (1999)

    Book  Google Scholar 

  28. Xia, R., Ding, Z.: Emotion-cause pair extraction: a new task to emotion analysis in texts. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1003–1012. Association for Computational Linguistics (2019)

    Google Scholar 

  29. Xie, J., Chen, B., Gu, X., Liang, F., Xu, X.: Self-attention-based BiLSTM model for short text fine-grained sentiment classification. IEEE Access 7, 180558–180570 (2019)

    Article  Google Scholar 

  30. Xu, B., Lin, H., Lin, Y., Diao, Y., Yang, L., Xu, K.: Extracting emotion causes using learning to rank methods from an information retrieval perspective. IEEE Access 7, 15573–15583 (2019)

    Article  Google Scholar 

  31. Xu, R., Hu, J., Lu, Q., Wu, D., Gui, L.: An ensemble approach for emotion cause detection with event extraction and multi-kernel SVMs. Tsinghua Sci. Technol. 22(6), 646–659 (2017)

    Article  Google Scholar 

  32. Xue, W., Zhou, W., Li, T., Wang, Q.: MTNA: a neural multi-task model for aspect category classification and aspect term extraction on restaurant reviews. In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 151–156 (2017)

    Google Scholar 

  33. Yang, M., Zhu, D., Chow, K.P.: A topic model for building fine-grained domain-specific emotion lexicon. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 421–426 (2014)

    Google Scholar 

  34. Yu, J., Marujo, L., Jiang, J., Karuturi, P., Brendel, W.: Improving multi-label emotion classification via sentiment classification with dual attention transfer network. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 1097–1102 (2018)

    Google Scholar 

  35. Zirn, C., Niepert, M., Stuckenschmidt, H., Strube, M.: Fine-grained sentiment analysis with structural features. In: Proceedings of 5th International Joint Conference on Natural Language Processing, pp. 336–344 (2011)

    Google Scholar 

Download references

Acknowledgements

This work was supported by Beijing Natural Science Foundation(4192057). We thank anonymous reviewers for their helpful feedback and suggestions.

Author information

Authors and Affiliations

  1. Beijing Language and Culture University, Beijing, 100083, China

    Hongliang Bi & Pengyuan Liu

  2. Language Resources Monitoring and Research Center, Beijing, 100083, China

    Hongliang Bi & Pengyuan Liu

Authors
  1. Hongliang Bi
    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. 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

Bi, H., Liu, P. (2020). Clue Extraction for Fine-Grained Emotion Analysis. 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_61

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60450-9_61

  • 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

Search

Navigation