Khaleel I. Al-Daoud
Department of Accounting, Business School, Al Ahliyya Amman University, Amman 19111, Jordan
Yogeesh N
Department of Mathematics, Government First Grade College, Tumkur, Karnataka 572102, India
Suleiman I. S. Mohammad
Department of Electronic Marketing and Social Media, Faculty of Economic and Administrative Sciences, Zarqa University, Zarqa 13132, Jordan
Faculty of Business and Communications, INTI International University, Negeri Sembilan 71800, Malaysia
N. Raja
Department of Visual Communication, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu 600119, India
R. Kavitha H S
Department of English, Government First Grade College and PG Center, Chintamani, Chikkaballapura 563125, India
Aravinda Reddy N
Department of English, Government College for Women, Chintamani, Chikkaballapur 563125, India
Asokan Vasudevan
Faculty of Business and Communications, INTI International University, Negeri Sembilan 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
Nawaf Alshdaifat
Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan
Mohammad F. A. Hunitie
Department of Public Administration, School of Business, University of Jordan, Amman 11942, Jordan
This study presents a novel fuzzy logic framework to quantitatively evaluate dialogue coherence, integrating mathematical modeling with an experimental case study approach. Recognizing that dialogue coherence is a continuous and multidimensional construct, we employ fuzzy set theory to design membership functions for critical linguistic variables, including topical continuity, syntactic alignment, and semantic relevance. Unlike traditional binary metrics, our approach computes a continuous coherence score using a weighted aggregation model, where each score is deriving expert-calibrated fuzzy inference rules. The empirical case study uses a heterogeneous dialogue corpus, consisting of interview transcripts and natural conversation recordings. The corpus was split into segments, with the segments annotated by linguistic experts. The Pearson correlation statistical analysis shows a strong correlation between the fuzzy coherence scores and the expert ratings, highlighting the robustness and reliability of the method. The research elaborates on implications for communication studies, such as applications to therapy, education, and human-computer interaction, as well as its limitations like subjectivity in defining the rules or challenges for scaling it. We conclude by proposing several lines of future research, such as incorporating additional variables spanning linguistic and non-verbal aspects and creating methods for automated calibration that would allow the model to personalize itself over time. In summary, our study confirms the usage of fuzzy logic systems with respect to the subtle gradience of dialogue coherence, enriching not just the theoretical notions of a dialogue but also being used as an exhaust model for classification.
[1] Zadeh, L.A., 1965. Fuzzy Sets. Information and Control. 8(3), 338–353.
[2] Klir, G.J., Yuan, B., 1995. Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall: Upper Saddle River, NJ, USA.
[3] Mamdani, E.H., 1974. Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers. 121(12), 1585–1588.
[4] Sun, K. and Xiong, W., 2019. A computational model for measuring discourse complexity. Discourse Studies. 21, 690-712. DOI: https://doi.org/10.1177/1461445619866985
[5] Wolf, F. and Gibson, E., 2006. Coherence in natural language: data structures and applications. MIT Press: Cambridge, MA, USA.
[6] Khoury, R., Karray, F., Sun, Y., et al., 2007. Semantic understanding of general linguistic items by means of fuzzy set theory. IEEE Transactions on fuzzy systems. 15, 757-771. DOI: https://doi.org/10.1109/TFUZZ.2006.889817
[7] Vashishtha, S., Gupta, V., Mittal, M., 2023. Sentiment analysis using fuzzy logic: A comprehensive literature review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 13, e1509. DOI: https://doi.org/10.1002/widm.1509
[8] Chen, Y., Zhao, J., Wen, Z., et al., 2024. Temporalmed: Advancing medical dialogues with time-aware responses in large language models. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining; 04 March 2024; Merida, Mexico. pp. 116-124. DOI: https://doi.org/10.1145/3616855.3635860
[9] Jackson, G.T., Castellano, K.E., Brockway, D., et al., 2018. Improving the measurement of cognitive skills through automated conversations. Journal of Research on Technology in Education. 50, 226-240. DOI: https://doi.org/10.1080/15391523.2018.1452655
[10] Doe, J., Roe, R., 2023. Cross-cultural fuzzy logic frameworks for dialogue analysis. Results in Engineering. 15, 101091. DOI: https://doi.org/10.1016/j.rineng.2023.101091
[11] Smith, A., Lee, B., 2023. Challenges in membership calibration for multilingual fuzzy systems. Results in Engineering. 15, 101483. DOI: https://doi.org/10.1016/j.rineng.2023.101483
[12] Choi, W.S., Kim, H., Seo, J., 2005. An integrated dialogue analysis model for determining speech acts and discourse structures. IEICE TRANSACTIONS on Information and Systems. 88, 150-157. DOI: http://dx.doi.org/10.1093/ietisy/E88-D.1.150
[13] Alejandro, A., Zhao, L., 2024. Multi-method qualitative text and discourse analysis: A methodological framework. Qualitative inquiry. 30, 461-473. DOI: https://doi.org/10.1177/10778004231184421
[14] Bibauw, S., Van den Noortgate, W., François, T., et al., 2022. Dialogue systems for language learning: A meta-analysis. Language Learning & Technology. 26, 1-24.
[15] Abbuhl, R., Gass, S., Mackey, A., 2013. Experimental research design. Research methods in linguistics. 1, 116-134.
[16] Rühlemann, C., O'Donnell, M.B., 2012. Introducing a corpus of conversational stories. Construction and annotation of the Narrative Corpus. Corpus Linguistics and Linguistic Theory. 8, 313-350.
[17] Kaliyaperumal, P., Das, A., 2022. A mathematical model for nonlinear optimization which attempts membership functions to address the uncertainties. Mathematics. 10, 1743. DOI: https://doi.org/10.3390/math10101743
[18] Varshney, A.K., Torra, V., 2023. Literature review of the recent trends and applications in various fuzzy rule-based systems. International Journal of Fuzzy Systems. 25, 2163-2186. DOI: https://doi.org/10.1007/s40815-023-01534-w
[19] Komatani, K., Okada, S., Nishimoto, H., et al., 2021. Multimodal dialogue data collection and analysis of annotation disagreement. In Increasing Naturalness and Flexibility in Spoken Dialogue Interaction: 10th International Workshop on Spoken Dialogue Systems. Springer: Singapore. pp. 201-213
[20] Arfi, B., 2010. Linguistic fuzzy logic methods in social sciences (Vol. 253). Springer: Berlin, Germany.
[21] Coppi, R., Gil, M.A., Kiers, H.A., 2006. The fuzzy approach to statistical analysis. Computational statistics & data analysis. 51, 1-14. DOI: https://doi.org/10.1016/j.csda.2006.05.012
[22] Chou, H.G., Chuang, C.F., Wang, W.J., et al., 2013. A fuzzy-model-based chaotic synchronization and its implementation on a secure communication system. IEEE transactions on information forensics and security. 8, 2177-2185. DOI: https://doi.org/10.1109/TIFS.2013.2286268
[23] Yaseen, H., Alsoud, A., Nofal, M., et al., 2021. The effects of online learning on students' performance: A comparison between UK and Jordanian universities. International Journal of Emerging Technologies in Learning. 16, 4-18.
[24] Shoaip, N., El-Sappagh, S., Abuhmed, T., et al., 2024. A dynamic fuzzy rule-based inference system using fuzzy inference with semantic reasoning. Scientific reports. 14, 4275. DOI: https://doi.org/10.1038/s41598-024-54065-1
[25] Abdul-Gader, A.H., Kozar, K.A., 1990. Discourse analysis for knowledge acquisition: the coherence method. Journal of Management Information Systems. 6, 61-82. DOI: https://doi.org/10.1080/07421222.1990.11517872
[26] Al-Rahmi, W.M., Al-Adwan, A.S., Al-Maatouk, Q., et al., 2023. Integrating communication and task–technology fit theories: The adoption of digital media in learning. Sustainability. 15, 8144. DOI: https://doi.org/10.3390/su15108144
[27] Kanagavalli, V.R., Raja, K., 2013. Handling Uncertain and Ambiguous Spatial Expressions in Text Using Fuzzy Logic. Indian Journal of Computer Science and Engineering. 4, 293-296.
[28] Isikdemir, Y.E., Yavuz, H.S., 2022. The Scalable Fuzzy Inference‐Based Ensemble Method for Sentiment Analysis. Computational Intelligence and Neuroscience. 2022, 5186144. DOI: https://doi.org/10.1155/2022/5186144
[29] Strachan, J.W., Albergo, D., Borghini, G., et al., 2024. Testing theory of mind in large language models and humans. Nature Human Behaviour. 8, 1285-1295. DOI: https://doi.org/10.1038/s41562-024-01882-z
[30] Mohammad, S.I.S., Jiang, H., Al Sarayreh, A., 2024. Research on Multimodal College English Teaching Model Based on Genetic Algorithm. Data and Metadata. 3, 421. DOI: https://doi.org/10.56294/dm2024421
[31] Mohammad, A.A.S., Mohammad, S.I.S., Al Oraini, B., et al., 2025. Data security in digital accounting: A logistic regression analysis of risk factors. International Journal of Innovative Research and Scientific Studies. 8, 2699-2709.
[32] Chen, T., Su, P., Shen, Y., et al., 2022. A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia. Frontiers in Neuroscience. 16, 867664. DOI: https://doi.org/10.3389/fnins.2022.867664
[33] Mohammad, A.A.S., Mohammad, S.I.S., Al‑Daoud, K.I., et al., 2025. Optimizing the Value Chain for Perishable Agricultural Commodities: A Strategic Approach for Jordan. Research on World Agricultural Economy. 6, 465-478. DOI: https://doi.org/10.36956/rwae.v6i1.1571
Copyright © 2025 Khaleel I. Al-Daoud, Yogeesh N, Suleiman I. S. Mohammad, N. Raja, R. Kavitha H S, Aravinda Reddy N, Asokan Vasudevan, Nawaf Alshdaifat, Mohammad F. A. Hunitie
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