Skip to main content
SpringerLink
Log in

Fi-Senti: A Language-Independent Model for Figurative Sentiment Analysis

  • Conference paper
  • First Online:
Computational Social Networks (CSoNet 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9795))

Included in the following conference series:

  • 1289 Accesses

Abstract

This paper focuses on identifying the polarity of figurative language in the very short text collected from Social Network Services. Although this topic is not new, most computer scientists have solved this issue by using natural language processing techniques. This seems difficult for non-native English speakers because they have to rely on heuristics in language. Therefore, our target in this work is to find a language-independent approach to solve the problem without using any semantic resources (e.g., dictionaries and ontologies). A statistical method based on two main features (i.e., (i) textual terms and (ii) sentimental patterns) is proposed to determine the sentiment degree of three popular types of figurative language (i.e., sarcasm, irony, and metaphor). We experimented on two Test sets with about 3,800 tweets and used Cosine similarity as the correlation measurement for evaluating the performance. The results show that our Fi-Senti model (Figurative Sentiment analysis model) well performs in determining the sentiment intensity of the figurative language with the best achievement is 0.8952 with sarcasm and 0.9011 with irony.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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://alt.qcri.org/semeval2015/.

References

  1. Amigó, E., et al.: Overview of RepLab 2013: evaluating online reputation monitoring systems. In: Forner, P., Müller, H., Paredes, R., Rosso, P., Stein, B. (eds.) CLEF 2013. LNCS, vol. 8138, pp. 333–352. Springer, Heidelberg (2013)

    Google Scholar 

  2. Davidov, D., Tsur, O., Rappoport, A.: Semi-supervised recognition of sarcastic sentences in twitter and amazon. In: Proceedings of the 14th Conference on Computational Natural Language Learning, pp. 107–116 (2010)

    Google Scholar 

  3. Dragoni, M.: Shellfbk: an information retrieval-based system for multi-domain sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation, pp. 502–509 (2015)

    Google Scholar 

  4. Giménez, M., Pla, F., Hurtado, L.-F.: Elirf: a SVM approach for SA tasks in twitter at SemEval-2015. In: Proceedings of the 9th International Workshop on Semantic Evaluation, pp. 574–581 (2015)

    Google Scholar 

  5. Hao, Y., Veale, T.: An ironic fist in a velvet glove: creative mis-representation in the construction of ironic similes. Mind. Mach. 20(4), 635–650 (2010)

    Article  Google Scholar 

  6. Long, N.H., Jung, J.J.: Privacy-aware framework for matching online social identities in multiple social networking services. Cybern. Syst. 46(1–2), 69–83 (2015)

    Article  Google Scholar 

  7. Karanasou, M., Doulkeridis, C., Halkidi, M.: Dsunipi: an SVM-based approach for sentiment analysis of figurative language on twitter. In: Proceedings of the 9th International Workshop on Semantic Evaluation, pp. 709–713 (2015)

    Google Scholar 

  8. Kaur, A., Gupta, V.: A survey on sentiment analysis and opinion mining techniques. J. Emerg. Technol. Web Intell. 5(4), 367–371 (2013)

    Google Scholar 

  9. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)

    Article  Google Scholar 

  10. Nguyen, H.L., Jung, J.E.: Statistical approach for figurative sentiment analysis on social networking services: a case study on twitter. Multimedia Tools Appl. (2016). doi:10.1007/s11042-016-3525-9

    Google Scholar 

  11. Özdemir, C., Bergler, S.: Clac-sentipipe: SemEval 2015 subtasks 10 b, e, and task 11. In: Proceedings of the 9th International Workshop on Semantic Evaluation, pp. 479–485 (2015)

    Google Scholar 

  12. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86 (2002)

    Google Scholar 

  13. Rajadesingan, A., Zafarani, R., Liu, H.: Sarcasm detection on twitter: a behavioral modeling approach. In: Proceedings of the 8th ACM International Conference on Web Search and Data Mining, pp. 97–106 (2015)

    Google Scholar 

  14. Reyes, A., Rosso, P.: Making objective decisions from subjective data: detecting irony in customers reviews. Decis. Support Syst. 53(4), 754–760 (2012)

    Article  Google Scholar 

  15. Reyes, A., Rosso, P.: On the difficulty of automatically detecting irony: beyond a simple case of negation. Knowl. Inf. Syst. 40(3), 595–614 (2014)

    Article  Google Scholar 

  16. Reyes, A., Rosso, P., Buscaldi, D.: From humor recognition to irony detection: the figurative language of social media. Data Knowl. Eng. 74, 1–12 (2012)

    Article  Google Scholar 

  17. Reyes, A., Rosso, P., Veale, T.: A multidimensional approach for detecting irony in twitter. Lang. Resour. Eval. 47(1), 239–268 (2013)

    Article  Google Scholar 

  18. Sang, E.T.K., Bos, J.: Predicting the 2011 Dutch Senate election results with twitter. In: Proceedings of the Workshop on Semantic Analysis in Social Media, pp. 53–60 (2012)

    Google Scholar 

  19. Shutova, E., Sun, L., Korhonen, A.: Metaphor identification using verb and noun clustering. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 1002–1010 (2010)

    Google Scholar 

  20. Tsur, O., Davidov, D., Rappoport, A.: ICWSM - a great catchy name: semi-supervised recognition of sarcastic sentences in online product reviews. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, pp. 162–169 (2010)

    Google Scholar 

  21. Veale, T.: Creative language retrieval: a robust hybrid of information retrieval and linguistic creativity. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 278–287 (2011)

    Google Scholar 

  22. Veale, T.: Detecting and generating ironic comparisons: an application of creative information retrieval. In: Proceedings of the AAAI Fall Symposium: Artificial Intelligence of Humor, pp. 101–108 (2012)

    Google Scholar 

  23. Veale, T., Hao, Y.: Comprehending and generating apt metaphors: a web-driven, case-based approach to figurative language. In: Proceedings of the 22nd National Conference on Artificial Intelligence, vol. 2, pp. 1471–1476 (2007)

    Google Scholar 

  24. Veale, T., Keane, M.T.: Conceptual scaffolding: a spatially-founded meaning representation for metaphor comprehension. Comput. Intell. 8(3), 494–519 (1992)

    Article  Google Scholar 

  25. Xu, H., Santus, E., Laszlo, A., Huang, C.-R.: LLT-PolyU: identifying sentiment intensity in ironic tweets. In: Proceedings of the 9th International Workshop on Semantic Evaluation, pp. 673–678 (2015)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A5B6037297). Also, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2014R1A2A2A05007154).

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Chung-Ang University, Seoul, Korea

    Hoang Long Nguyen & Jason J. Jung

  2. Faculty of Information Technology, Vietnam Maritime University, Haiphong, Vietnam

    Trung Duc Nguyen

Authors
  1. Hoang Long Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Trung Duc Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jason J. Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jason J. Jung .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University , Ho Chi Minh City, Vietnam

    Hien T. Nguyen

  2. VSB-Technical University of Ostrava , Ostrava, Poland

    Vaclav Snasel

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Nguyen, H.L., Nguyen, T.D., Jung, J.J. (2016). Fi-Senti: A Language-Independent Model for Figurative Sentiment Analysis. In: Nguyen, H., Snasel, V. (eds) Computational Social Networks. CSoNet 2016. Lecture Notes in Computer Science(), vol 9795. Springer, Cham. https://doi.org/10.1007/978-3-319-42345-6_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42345-6_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42344-9

  • Online ISBN: 978-3-319-42345-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation