Modeling Semantic Gradience in Natural Language through Fuzzy Set Theory

Authors

  • Yogeesh Nijalingappa

    Department of Mathematics, Government First Grade College, Tumkur 572201, India
  • Suleiman Ibrahim Mohammad

    Electronic Marketing and Social Media, Faculty of Economic and Administrative Sciences, Zarqa University, Zarqa 13115, Jordan

    Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia
  • Khaleel Ibrahim Al- Daoud

    Department of Accounting, Business School Faculties, Al Ahilya Amman University, Amman 19111, Jordan
  • Natarajan Raja

    Department of Visual Communication, Sathyabama Institute of Science and Technology, Chennai 600119, India
  • Manoj Chandrappa

    Department of English, Government First Grade College Kunigal, Kunigal 572130, India
  • Raghavendra Honnavalli Mallappa

    Government First Grade College, Tumkur 572201, Karnataka, India
  • Asokan Vasudevan

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

    Faculty of Management, Shinawatra University, 99 Moo 10, Bangtoey, Samkhok 12160, Thailand

    Business Administration and Management, Wekerle Business School, Jázmin u. 10, 1083 Budapest, Hungary
  • Mohammad Faleh Ahmmad Hunitie

    Department of Public Administration, School of Business, University of Jordan, Amman 11942, Jordan
  • Nawaf Alshdaifat

    Faculty of Information Technology, Applied Science Private University, Amman 11937, Jordan
  • Parameshwaran Chandra Segaran

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

DOI:

https://doi.org/10.30564/fls.v7i6.9619
Received: 20 April 2025 | Revised: 13 May 2025 | Accepted: 21 May 2025 | Published Online: 10 June 2025

Abstract

 This study explores semantic gradience in natural language by employing fuzzy-set theory to quantitatively to model the continuum of meaning inherent in emotional adjectives. Focusing on the term “happy”,  we conducted an experimental case study with 30 participants who rated the word on a 7 -point Likert scale. The raw ratings were normalized to a  interval, yielding a mean normalized rating of approximately 0.617 and a standard deviation of about 0.228. A Gaussian fuzzy membership function was derived using these empirical parameters, which effectively captures the smooth transition of membership degrees and the inherent fuzzy boundaries in semantic interpretation. The findings suggest that the meaning of “happy" isn’t simply a binary result; instead, there is a range of degrees of semantic similarity, with vast areas of overlap that conventional categorization approaches don't account for. This quantitative perspective leads to an interface between cognitive-functional linguistics and fuzzy mathematics, which thoroughly expands the insights into linguistic variation. The findings show that fuzzy set theory can enhance the theoretical models used to explain semantics in linguistics and provide useful insights for practitioners in the fields of computational linguistics and natural language processing. Further research extending this methodology to other semantic domains and including more diverse participant samples is warranted to further validate and refine the model.

Keywords:

Semantic Gradience; Fuzzy Set Theory; Emotional Adjectives; Gaussian Membership Function; Linguistic Variability; Natural Language Processing; Process Innovation; Education

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How to Cite

Nijalingappa, Y., Mohammad, S. I., Al- Daoud, K. I., Raja, N., Chandrappa, M., Mallappa, R. H., Vasudevan, A., Hunitie, M. F. A., Alshdaifat, N., & Segaran, P. C. (2025). Modeling Semantic Gradience in Natural Language through Fuzzy Set Theory. Forum for Linguistic Studies, 7(6), 789–803. https://doi.org/10.30564/fls.v7i6.9619

Issue

Vol. 7 , Iss. 6 (July 2025)

Article Type

Article

License

Copyright © 2025 Yogeesh Nijalingappa, Suleiman Ibrahim Mohammad, Khaleel Ibrahim Al- Daoud, Natarajan Raja, Manoj Chandrappa, Raghavendra Honnavalli Mallappa, Asokan Vasudevan, Mohammad Faleh Ahmmad Hunitie, Nawaf Alshdaifat, Parameshwaran Chandra Segaran

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