Skip to main content
SpringerLink
Log in

Fuzzy Natural Logic for Sentiment Analysis: A Proposal

  • Conference paper
  • First Online:
Distributed Computing and Artificial Intelligence, Volume 2: Special Sessions 18th International Conference (DCAI 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 332))

Abstract

Fuzzy Natural Logic (FNL) is introduced as a model that could be useful in the area of sentiment analysis. FNL is a formal theory of human reasoning that includes mathematical models of the semantics of natural language expressions with regard to the vagueness phenomenon. The most elaborated constituent of FNL is the theory of evaluative linguistic expressions. To capture their semantics, it uses a single scale for computing extension of any evaluative expression that might be relevant for sentiment analysis. Therefore, it provides a more fine-grained classification of opinion and sentiments than dichotomous models which only distinguish between ‘positive’ and ‘negative’ values.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, pp. 2200–2204 (2010)

    Google Scholar 

  2. Goddard, C., Wierzbicka, A.: Meaning and Universal Grammar: Theory and Empirical Findings, vol. 1. John Benjamins Publishing, Amsterdam (2002)

    Google Scholar 

  3. Hemmatian, F., Sohrabi, M.K.: A survey on classification techniques for opinion mining and sentiment analysis. Artif. Intell. Rev. 52(3), 1495–1545 (2017). https://doi.org/10.1007/s10462-017-9599-6

    Article  Google Scholar 

  4. Joshi, S., Mehta, S., Mestry, P., Save, A.: A new approach to target dependent sentiment analysis with onto-fuzzy logic. In: 2016 IEEE International Conference on Engineering and Technology (ICETECH), pp. 730–735. IEEE (2016)

    Google Scholar 

  5. Lakoff, G.: Linguistics and natural logic. Synthese 22(1–2), 151–271 (1970)

    Article  Google Scholar 

  6. Liu, B.: Sentiment Analysis: Mining Opinions. Sentiments and Emotions. Cambridge University Press, Cambridge (2020)

    Book  Google Scholar 

  7. Liu, H., Cocea, M.: Fuzzy rule based systems for interpretable sentiment analysis. In: 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI), pp. 129–136. IEEE (2017)

    Google Scholar 

  8. Mohammad, S., Dunne, C., Dorr, B.: Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus. In: Proceedings of the 2009 conference on empirical methods in natural language processing, pp. 599–608 (2009)

    Google Scholar 

  9. Murinová, P., Novák, V.: The theory of intermediate quantifiers in fuzzy natural logic revisited and the model of “Many." Fuzzy Sets Syst. 388, 56–89 (2020)

    Google Scholar 

  10. Nadali, S., Murad, M.A.A., Kadir, R.A.: Sentiment classification of customer reviews based on fuzzy logic. In: 2010 International Symposium on Information Technology, vol. 2, pp. 1037–1044. IEEE (2010)

    Google Scholar 

  11. Novák, V.: Mathematical fuzzy logic: from vagueness to commonsese reasoning. In: Kreuzbauer, G., Gratzl, N., Hielb, E. (eds.) Retorische Wissenschaft: Rede und Argumentation in Theorie und Praxis, pp. 191–223. LIT-Verlag, Wien (2008)

    Google Scholar 

  12. Novák, V.: Evaluative linguistic expressions vs. fuzzy categories? Fuzzy Sets Syst. 281, 81–87 (2015)

    Article  MathSciNet  Google Scholar 

  13. Novák, V.: Fuzzy natural logic: towards mathematical logic of human reasoning. In: Seising, R., Trillas, E., Kacprzyk, J. (eds.) Towards the Future of Fuzzy Logic. Studies in Fuzziness and Soft Computing, vol. 325, pp. 137–165. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18750-1_8

  14. Novák, V.: What is fuzzy natural logic. In: Huynh, V.N., Inuiguchi, M., Demoeux, T. (eds.) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2015. Lecture Notes in Computer Science, vol. 9376, pp. 15–18. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25135-6_3

  15. Novák, V.: Fuzzy logic in natural language processing. In: Proceedings of the International Conference on FUZZ-IEEE 2017. Naples, Italy (2017)

    Google Scholar 

  16. Novák, V.: The concept of linguistic variable revisited. In: Shahbazova, S.N., Sugeno, M., Kacprzyk, J. (eds.) Recent Developments in Fuzzy Logic and Fuzzy Sets. SFSC, vol. 391, pp. 105–118. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38893-5_6

  17. Novák, V., Murinová, P.: A formal model of the intermediate quantifiers “a few, several, a little.” In: Kearfott, R., Batyrshin, I., Reformat, M., Ceberio, M., Kreinovich, V. (eds.) Fuzzy Techniques: Theory and Applications, pp. 429–441. Springer, Cham, Switzerland (2019)

    Google Scholar 

  18. Novák, V., Murinová, P., Boffa, S.: On the properties of intermediate quantifiers and the quantifier “MORE-THAN”. In: Lesot, M.J., et al. (eds.) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020. Communications in Computer and Information Science, vol. 1239, pp. 159–172. Springer, Cham. https://doi.org/10.1007/978-3-030-50153-2_12

  19. Novák, V., Perfilieva, I., Dvořák, A.: Insight into Fuzzy Modeling. Wiley & Sons, Hoboken, New Jersey (2016)

    Book  Google Scholar 

  20. Taboada, M.: Sentiment analysis: an overview from linguistics. Annu. Rev. Ling. 2, 325–347 (2016)

    Article  Google Scholar 

  21. Urrutia, A.T.: An approach to measuring complexity within the boundaries of a natural language fuzzy grammar. In: Rodríguez, S., et al. (eds.) Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol. 801, pp. 222–230. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99608-0_25

  22. Urrutia, A.T.: A formal characterization of fuzzy degrees of grammaticality for natural language. Ph.D. thesis, Universitat Rovira i Virgili (2019)

    Google Scholar 

  23. Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning subjective language. Comput. Ling. 30(3), 277–308 (2004)

    Article  Google Scholar 

  24. Wierzbicka, A.: Semantics: Primes and Universals: Primes and Universals. Oxford University Press, Oxford, UK (1996)

    Google Scholar 

  25. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 347–354 (2005)

    Google Scholar 

  26. Yadav, A., Vishwakarma, D.K.: Sentiment analysis using deep learning architectures: a review. Artif. Intell. Rev. 53(6), 4335–4385 (2019). https://doi.org/10.1007/s10462-019-09794-5

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Zadeh, L.A.: A fuzzy-set-theoretic interpretation of linguistic hedges. J. Cybern. 2(3), 4–34 (1972)

    Article  MathSciNet  Google Scholar 

  29. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning-I. Inf. Sci. 8(3), 199–249 (1975). https://doi.org/10.1016/0020-0255(75)90036-5

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgment

The project “Strengthening scientific capacities OU II.” has supported this paper. Very special thanks to Prof. Maite Taboada for her support during this research.

Author information

Authors and Affiliations

  1. University of Ostrava-IRAFM, 30. dubna 22, 701 03, Ostrava, Czech Republic

    Adrià Torrens Urrutia & Vilém Novák

  2. Universitat Rovira i Virgili-GRLMC, Av. Catalunya, 35, 43002, Tarragona, Spain

    M. Dolores Jiménez-López

Authors
  1. Adrià Torrens Urrutia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Dolores Jiménez-López
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vilém Novák
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adrià Torrens Urrutia .

Editor information

Editors and Affiliations

  1. BISITE, Digital Innovation Hub, University of Salamanca, Salamanca, Spain

    Sara Rodríguez González

  2. Centro ALGORITMI, Universidade do Minho, Braga, Portugal

    José Manuel Machado

  3. BISITE, University of Salamanca, Salamanca, Spain

    Alfonso González-Briones

  4. aleja Tysiąclecia Państwa Polskiego 7, Kielce University of Technology, Kielce, Poland

    Jaroslaw Wikarek

  5. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Roussanka Loukanova

  6. Cerca Trova Ltd, Sofia, Bulgaria

    George Katranas

  7. BISITE, Digital Innovation Hub, University of Salamanca, Salamanca, Salamanca, Spain

    Roberto Casado-Vara

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Torrens Urrutia, A., Jiménez-López, M.D., Novák, V. (2022). Fuzzy Natural Logic for Sentiment Analysis: A Proposal. In: González, S.R., et al. Distributed Computing and Artificial Intelligence, Volume 2: Special Sessions 18th International Conference. DCAI 2021. Lecture Notes in Networks and Systems, vol 332. Springer, Cham. https://doi.org/10.1007/978-3-030-86887-1_6

Download citation

Publish with us

Policies and ethics

Search

Navigation