Quantifying Metaphorical Language in Literature Using Fuzzy Semantics

Authors

  • Yogeesh Nijalingappa

    Department of Mathematics, Government First Grade College, Tumkur, Karnataka 572101, India
  • Suleiman Ibrahim Shelash Mohammad

    Electronic Marketing and Social Media, Economic and Administrative Sciences, Zarqa University, Zarqa 13132, Jordan

    Faculty of Business and Communications , INTI International University, Nilai, Negeri Sembilan 71800, Malaysia
  • Natarajan Raja

    Department of Visual Communication, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu 600119, India
  • Rayeesul Hassan Syed

    Department of English, Government College for Women, Kolar, Karnataka 563101, India
  • Kavitha Hanumenahalli Siddalingappa

    Department of English, Govt. First Grade College and PG Center, Chintamani, Karnataka 563125, India
  • Asokan Vasudevan

    Faculty of Business and Communications , INTI International University, Nilai, Negeri Sembilan 71800, Malaysia
  • Nawaf Alshdaifat

    Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordana
  • Mohammad Faleh Ahmmad Hunitie

    Department of Public Administration, School of Business, University of Jordan, Amman 11942, Jordan
  • Dheifallah Ibrahim Mohammad

    Faculty of Business and Communications , INTI International University, Nilai, Negeri Sembilan 71800, Malaysia

DOI:

https://doi.org/10.30564/fls.v7i6.9441
Received: 11 April 2025 | Revised: 28 April 2025 | Accepted: 7 May 2025 | Published Online: 13 June 2025

Abstract

Using fuzzy semantics—a framework derived within cognitive linguistics that defines the way in which entities relate to objects—we introduce empirical justification for our parameter weights (0.3, 0.4, 0.3) based on a pilot study of 50 metaphor instances. We present a new approach for quantifying metaphorical language in centuries of literary writings. We deal with the inherent gradience of metaphoric expression by computing Fuzzy Membership Values (FMVs) from three core parameters: literalness, vividness, and abstraction. The analysis was complemented with the Metaphor Identification Procedure (MIP), and expert evaluations reveal insights into metaphoric use for a small-scale case study of selected verses by Sylvia Plath, Emily Dickinson, and an Indian English poet. Extended metaphors, novel sensory mappings, and conventional metaphors each produce different FMV ranges, offering an objective metric to demonstrate nuanced differentiations in metaphorical force. This quantitative manner complements traditional qualitative analyses and provides a replicable, scalable tool for literary criticism. We also report inter-rater reliability (Cohen’s κ = 0.82) and include updated figures (actual plots, not placeholders) for membership functions and α-cuts. Potential applications in educational and digital humanities are discussed, alongside future avenues such as automated NLP assignments and cross-linguistic validation. Including the AMI and sensitivity analysis strengthens methodological rigor and sets clear benchmarks for subsequent work.

Keywords:

Fuzzy Semantics; Linguistic Gradience; Metaphor Analysis; Literary Criticism; Cognitive Linguistics; Digital Humanities

References

[1] Aristotle, 2019. Poetics [Butcher, S.H., Trans]. Open Source Publications: New York, NY, USA.

[2] Moretti, F., 2013. Distant Reading. Verso Books: London, UK.

[3] Quintilian, 1920. Institutes of Oratory [in Latin] [Butler, H.E. Trans.]. Harvard University Press: Cambridge, MA, USA.

[4] Cicero, 55 BCE. On the Orator [in Latin]. Loeb Classical Library: Cambridge, MA, USA.

[5] Lakoff, G., Johnson, M., 1980. Metaphors We Live By. University of Chicago Press: Chicago, IL, USA.

[6] Richards, I.A., 1936. The Philosophy of Rhetoric. Oxford University Press: Oxford, UK.

[7] Kövecses, Z., 2017. Where Metaphors Come From: Reconsidering Context in Metaphor. Oxford University Press: Oxford, UK.

[8] Mohammed, S., Rico, R., Alipour, K.K, 2021. Team cognition at a crossroad: Toward conceptual integration and network configurations. Academy of Management Annals. 15(2), 455–501.

[9] Fauconnier, G., Turner, M., 2019. The Way We Think: Conceptual Blending and the Mind's Hidden Complexities, 2nd ed. Basic Books: New York, NY, USA.

[10] Cameron, L., Maslen, R. (eds.), 2010. Metaphor Analysis: Research Practice in Applied Linguistics, Social Sciences and the Humanities. Equinox: Sheffield, UK.

[11] Russell, B., 1908. Mathematical logic as based on the theory of types. American Journal of Mathematics. 30(3), 222–262.

[12] Black, M., 1962. Models and Metaphors: Studies in Language and Philosophy. Cornell University Press: Ithaca, NY, USA.

[13] Zadeh, L.A., 1965. Fuzzy sets. Information and Control. 8(3), 338–353.

[14] Kosko, B., 1986. Fuzzy cognitive maps. International Journal of Man-Machine Studies. 24(1), 65–75.

[15] Stubbs, M., 2005. Conrad in the computer: Examples of quantitative stylistic methods. Language and Literature. 14(1), 5–24.

[16] Fauconnier, G., Turner, M., 1998. Conceptual integration networks. Cognitive science. 22(2), 133-187.

[17] Steen, G.J., Dorst, A.G., Krennmayr, T., et al., 2010. A method for linguistic metaphor identification. John Benjamins B.V.: Amsterdam, Netherlands.

[18] Babieno, M., Takeshita, M., Radisavljevic, D., Rzepka, R., Araki, K., 2022. Miss roberta wilde: Metaphor identification using masked language model with wiktionary lexical definitions. Applied Sciences. 12(4), 2081.

[19] Sharma, R., Vashisht, V., Singh, U., 2022. eeFFA/DE-a fuzzy-based clustering algorithm using hybrid technique for wireless sensor networks. International Journal of Advanced Intelligence Paradigms. 21(1-2), 129-157.

[20] Klir, G.J., Yuan, B., 1995. Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall: Upper Saddle River, NJ, USA.

[21] Karim, M.A., Abdullah, M.Z., Deifalla, A.F., Azab, M., Waqar, A., 2023. An assessment of the processing parameters and application of fibre-reinforced polymers (FRPs) in the petroleum and natural gas industries: A review. Results in Engineering. 18, 101091.

[22] Karim, M.A., Abdullah, M.Z., Waqar, A., Deifalla, A.F., Ragab, A.E., Khan, M., 2023. Analysis of the mechanical properties of the single layered braid reinforced thermoplastic pipe (BRTP) for oil & gas industries. Results in Engineering. 20, 101483.

[23] Pragglejaz Group, 2007. MIP: A method for identifying metaphorically used words in discourse. Metaphor and Symbol. 22(1), 1–39. DOI: https://doi.org/10.1080/10926480709336752

[24] Forceville, C., 2002. Pictorial Metaphor in Advertising. Routledge: London, UK.

[25] Kacprzyk, J., Yager, R.R., Zadrożny, S., 2000. A fuzzy logic based approach to linguistic summaries of databases. International Journal of Applied Mathematics and Computer Science. 10(4), 813-834.

[26] Brown, S., Wijland, R., 2018. Figuratively speaking: of metaphor, simile and metonymy in marketing thought. European Journal of Marketing. 52(1/2), 328-347.

[27] Gupta, S. 2013. Big issues around a small-scale phenomenon: Vernacular pulp fiction in English translation for Indian readers. The Journal of Commonwealth Literature. 48(1), 159-175.

[28] Coronato, R., Gesuato, S., 2019. The Literature-Linguistics Interface--Bridging the Gap Between Qualitative and Quantitative Approaches to Literary Texts. Umanistica Digitale. 6, 1-7.

[29] Sheramatovich, B.A., 2024. Comparative Analysis of Metaphorical Patterns. Multidisciplinary Journal of Science and Technology. 4(5), 230-234.

[30] Mukhin, V., Zavgorodnii, V., Liskin, V., et al., 2025. A model for classifying information objects using neural networks and fuzzy logic. Scientific Reports. 15(1), 1-15.

[31] Baddane, K., Ennam, A., 2024. Measuring pedagogical transformation: A quantitative analysis of critical thinking integration in literary criticism for heightened student engagement and learning outcomes. International Journal of Linguistics, Literature and Translation. 7(1), 39-50.

[32] Alcalá, R., Gacto, M. J., Herrera, F., 2011. A fast and scalable multiobjective genetic fuzzy system for linguistic fuzzy modeling in high-dimensional regression problems. IEEE Transactions on Fuzzy Systems. 19(4), 666-681.

[33] Sarfraz, S., Fazal, N., 2024. Automated Sentiment Analysis of Linguistic and Emotional Dimensions in The Book of Dede Korkut and Fuzzy Logic. Annals of Human and Social Sciences. 5(4), 454-464.

[34] Xiong, H., Li, L., Qu, Y., 2015. Exploring EFL teachers' cognitive models through metaphor analysis. SAGE Open. 5(4), 2158244015608900.

[35] Mendel, J.M., 2001. Uncertain Rule-Based Fuzzy Logic Systems. Prentice Hall: Upper Saddle River, NJ, USA.

Downloads

How to Cite

Nijalingappa, Y., Shelash Mohammad, S. I., Raja, N., Syed, R. H., Siddalingappa, K. H., Vasudevan, A., Alshdaifat, N., Ahmmad Hunitie, M. F., & Mohammad, D. I. (2025). Quantifying Metaphorical Language in Literature Using Fuzzy Semantics. Forum for Linguistic Studies, 7(6), 895–909. https://doi.org/10.30564/fls.v7i6.9441

Issue

Vol. 7 , Iss. 6 (June 2025): In progress

Article Type

Article

License

Copyright © 2025 Yogeesh Nijalingappa, Suleiman Ibrahim Shelash Mohammad, Natarajan Raja, Rayeesul Hassan Syed, Kavitha Hanumenahalli Siddalingappa, Asokan Vasudevan, Nawaf Alshdaifat, Mohammad Faleh Ahmmad Hunitie, Dheifallah Ibrahim Mohammad

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.