Skip to main content
SpringerLink
Log in

Fuzzy Logic Based Dynamic Plotting of Mood Swings from Tweets

  • Conference paper
  • First Online:
Innovations in Bio-Inspired Computing and Applications (IBICA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 939))

Abstract

Twitter is one the most popular social media platforms. Users express their feelings easily on social media regarding any trending event. In this paper, we propose a fuzzy logic based approach for dynamic plotting of mood swings from tweets. The novelty of the paper is use of linguistic hedges with fuzzy logic to compute the sentiment of tweet. Comparison of our approach with existing methods, on real-time tweets extracted from online website confirms the superiority and efficiency of our method. The tweets used in our experiments are extracted from the timeline of the India Vs Pakistan final ICC world-cup match in June 2017. They reflect the moods of the twitter users as the match progresses. Using our fuzzy logic based approach, we successfully plot the dynamic mood vs time and compute the polarity of the sentiment at each time instant.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

References

  1. Microfocus. https://blog.microfocus.com/how-much-data-is-created-on-the-internet-each-day/. Accessed 12 Sept 2018

  2. Zhao, L., Chen, F., Lu, C.T., Ramakrishnan, N.: Dynamic theme tracking in Twitter. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 561–570. IEEE (2015)

    Google Scholar 

  3. Marquez, F.B., Avello, D.G., Mendoza, M., Poblete, B.: Opinion dynamics of elections in Twitter. In: 2012 Eighth Latin American Web Congress (LA-WEB), pp. 32–39. IEEE (2012)

    Google Scholar 

  4. Irfan, R., King, C.K., Grages, D., Ewen, S., Khan, S.U., Madani, S.A., Kolodziej, J.: A survey on text mining in social networks. Knowl. Eng. Rev. 30(2), 157–170 (2015)

    Article  Google Scholar 

  5. Chowdhury, G.G.: Natural language processing. Ann. Rev. Inf. Sci. Technol. 37(1), 51–89 (2003)

    Article  MathSciNet  Google Scholar 

  6. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  Google Scholar 

  7. Zadeh, L.A.: Calculus of fuzzy restrictions. In: Fuzzy Sets and Their Applications to Cognitive and Decision Processes, pp. 1–40 (1975)

    Google Scholar 

  8. Ross, T.J.: Fuzzy Logic with Engineering Applications. Wiley, Hoboken (2009)

    Google Scholar 

  9. Saquib, S., Ali, R.: Understanding dynamics of trending topics in Twitter. In: 2017 International Conference on Computing, Communication and Automation (ICCCA), pp. 98–103. IEEE (2017)

    Google Scholar 

  10. Ghiassi, M., Skinner, J., Zimbra, D.: Twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst. Appl. 40(16), 6266–6282 (2013)

    Article  Google Scholar 

  11. Zimbra, D., Ghiassi, M., Lee, S.: Brand-related twitter sentiment analysis using feature engineering and the dynamic architecture for artificial neural networks. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), pp. 1930–1938. IEEE (2016)

    Google Scholar 

  12. Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In: Proceedings of the ACL 2012 System Demonstrations, pp. 115–120. Association for Computational Linguistics (2012)

    Google Scholar 

  13. Daher, L.A., Zantout, R., Elkabani, I.: Dynamic evolution of hashtags on Twitter: a case study from career opportunities groups. In: 2017 International Conference on New Trends in Computing Sciences (ICTCS), pp. 314–319. IEEE (2017)

    Google Scholar 

  14. Cunha, E., Magno, G., Comarela, G., Almeida, V., Gonçalves, M.A., Benevenuto, F.: Analyzing the dynamic evolution of hashtags on Twitter: a language-based approach. In: Proceedings of the Workshop on Languages in Social Media, pp. 58–65. Association for Computational Linguistics (2011)

    Google Scholar 

  15. Liu, S., Cheng, X., Li, F., Li, F.: TASC: topic-adaptive sentiment classification on dynamic tweets. IEEE Trans. Knowl. Data Eng. 27(6), 1696–1709 (2015)

    Article  Google Scholar 

  16. Chen, C., Wang, Z., Li, W.: Tracking dynamics of opinion behaviors with a content-based sequential opinion influence model. IEEE Trans. Affect. Comput. 1, 1 (2018)

    Google Scholar 

  17. SentiWordNet. http://sentiwordnet.isti.cnr.it/. Accessed 12 Sept 2018

  18. Natural Language Toolkit. http://www.nltk.org/#natural-language-toolkit. Accessed 12 Sept 2018

  19. Hameed, I.A.: Enhanced fuzzy system for student’s academic evaluation using linguistic hedges. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE (2017)

    Google Scholar 

  20. Zamali, T., Lazim, M.A., Osman, M.T.A.: Sensitivity analysis using fuzzy linguistic hedges. In: 2012 IEEE Symposium on Humanities, Science and Engineering Research (SHUSER), pp. 669–672. IEEE (2012)

    Google Scholar 

  21. Cavalcanti, D.C., Prudêncio, R.B.C., Pradhan, S.S., Shah, J.Y., Pietrobon, R.S.: Good to be bad? Distinguishing between positive and negative citations in scientific impact. In: 2011 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 156–162. IEEE (2011)

    Google Scholar 

  22. Ortega, R., Fonseca, A., Gutierrez, Y., Montoyo, A.: SSA-UO: unsupervised twitter sentiment analysis. In: Second Joint Conference on Lexical and Computational Semantics (* SEM), vol. 2, pp. 501–507 (2013)

    Google Scholar 

  23. Scoopwhoop. https://www.scoopwhoop.com/48-Tweets-Which-Define-How-Embarrassing-Indias-Champions-Trophy-Defeat-To-Pakistan-Was/#.l1uoc3uu5. Accessed 12 Sept 2018

  24. Twitter. https://twitter.com/. Accessed 12 Sept 2018

  25. File. https://drive.google.com/file/d/1Gz9Toz7GcfRYPfMw_S3zFd6wRhLDVmgC/view?usp=sharing. Accessed 31 Oct 2018

Download references

Author information

Authors and Affiliations

  1. Delhi Technological University, Delhi, 110042, India

    Srishti Vashishtha & Seba Susan

Authors
  1. Srishti Vashishtha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seba Susan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Srishti Vashishtha .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

  3. Department of Applied Science and Engineering, Indian Institute of Technology, Roorkee, India

    Millie Pant

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vashishtha, S., Susan, S. (2019). Fuzzy Logic Based Dynamic Plotting of Mood Swings from Tweets. In: Abraham, A., Gandhi, N., Pant, M. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2018. Advances in Intelligent Systems and Computing, vol 939. Springer, Cham. https://doi.org/10.1007/978-3-030-16681-6_13

Download citation

Publish with us

Policies and ethics

Search

Navigation