Advertisement

Abstract

Current research on emotion detection focuses on the recognizing explicit emotion expressions in text. In this paper, we propose an approach based on textual inference to detect implicit emotion expressions, that is, to capture emotion detection as an logical inference issue. The approach builds a natural logic system, in which emotional detection are decomposed into a series of logical inference process. The system also employ inference knowledge from textural inference resources for reasoning complex expressions in emotional texts. Experimental results show the efficiency in detecting implicit emotional expressions.

Keywords

Natural logic Textual inference Emotion detection Implicit emotional expression 

Notes

Acknowledgements

This work is supported by National Natural Science Foundation of China(61402341, 61402119) and Bidding Project of GDUFS Laboratory of Language Engineering and Computing(LEC2016ZBKT001, LEC2016ZBKT002).

References

  1. 1.
    Das, D., Bandyopadhyay, S.: Emotion analysis on social media: natural language processing approaches and applications. In: Agarwal, N., Lim, M., Wigand, Rolf T. (eds.) Online Collective Action. LNSN, pp. 19–37. Springer, Vienna (2014). doi: 10.1007/978-3-7091-1340-0_2 Google Scholar
  2. 2.
    Rao, K.S., Koolagudi, S.G.: Robust Emotion Recognition Using Spectral and Prosodic Features. Springer, New York (2013)zbMATHGoogle Scholar
  3. 3.
    Reisenzein, R., Hudlicka, E., Dastani, M., Gratch, J., Hindriks, K., Lorini, E., et al.: Computational modeling of emotion: toward improving the inter- and intradisciplinary exchange. IEEE Trans. Affect. Comput. 4(3) (2013)Google Scholar
  4. 4.
    Xu, J., Xu, R., Lu, Q., Wang, X.: coarse-to-fine sentence-level emotion classification based on the intra-sentence features and sentential context. In: CIKM2012, Maui, USA (2012)Google Scholar
  5. 5.
    Xu, R., Gui, L., Xu, J., Lu, Q., Wong, K.F.: Cross lingual opinion holder extraction based on multiple kernel SVMs and transfer learning. Int. J. World Wide Web 18(2) (2013)Google Scholar
  6. 6.
    Andreevskaia, A., Concordia, S.B.: Mining WordNet for a fuzzy sentiment: sentiment tag extraction from WordNet glosses. In: EACL 2006 (2006)Google Scholar
  7. 7.
    Xu, R., Wong, F.F.: Coarse-fine opinion mining - WIA in NTCIR-7 MOAT task. In: NTCIR-7 Workshop, Tokyo, Japan (2008)Google Scholar
  8. 8.
    Androutsopoulos, I., Malakasiotis, P.: A survey of paraphrasing and textul entailment methods. J. Artif. Intell. Res. 38(1), 135–187 (2010)zbMATHGoogle Scholar
  9. 9.
    MacCartney, B., Manning, C.D.: An extended model of natural logic. In: Proceedings of the 8th International Conference on Computational Semantics, Tilburg, Netherland (2009)Google Scholar
  10. 10.
    Torii, Y., Das, D., Bandyopadhyay, S., Okumura, M.: Developing Japanese WordNet affect for analyzing emotions. In: Proceedings of The 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, Portland, Oregon (2011)Google Scholar
  11. 11.
    Santos, C.N.D., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of the COLING 2015, Dublin, Ireland (2014)Google Scholar
  12. 12.
    Wang, X., Jiang, W., Luo, Z.: Combination of convolutional and recurrent neural network for sentiment analysis of short texts. In: Proceedings of COLING 2016, Osaka, Japan (2016)Google Scholar
  13. 13.
    Wang, Y., Huang, M., Zhao, L., Zhu, X.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, Texas (2016)Google Scholar
  14. 14.
    MacCartney, B., Manning, C.D.: An extended model of natural logic. In: Proceedings of the 8th International Conference on Computational Semantics (2009)Google Scholar
  15. 15.
    Benthem, J.V.: Essays in logical semantics. Studies in Linguistics and Philosophy, vol. 29. Springer, Dordrecht (1986)CrossRefGoogle Scholar
  16. 16.
    Valencia, V.M.S.: Studies on natural logic and categorial grammar. Ph.D. Thesis, University of Amsterdam (1991)Google Scholar
  17. 17.
    MacCartney, B., Manning, C.D.: Natural logic for textual inference. In: ACL-PASCAL Workshop on Textual Entailment and Paraphrasing (2007)Google Scholar
  18. 18.
    Angeli, G., Manning, C.D.: NaturalLI: natural logic inference for common sense reasoning. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Han Ren
    • 1
  • Yafeng Ren
    • 2
  • Xia Li
    • 1
  • Wenhe Feng
    • 1
  • Maofu Liu
    • 3
  1. 1.Laboratory of Language Engineering and ComputingGuangdong University of Foreign StudiesGuangzhouChina
  2. 2.Collaborative Innovation Center for Language Research and ServicesGuangdong University of Foreign StudiesGuangzhouChina
  3. 3.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina

Personalised recommendations