International Journal of Neutrosophic Science

Journal DOI

https://doi.org/10.54216/IJNS

Submit Your Paper

2690-6805ISSN (Online) 2692-6148ISSN (Print)

Volume 26 , Issue 1 , PP: 365-390, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

Enhancing educational environments with Social Media Feedback Evaluation Employing Hybrid Neutrosophic Decision Optimization (HNDO) and Neutrosophic Sentiment Fusion (NSF)

Walaa Fouda 1 * , Asmaa Hegazy 2 , Najla M. Alnaqbi 3 , Ebru Ozbilge 4 , Emre Özbilge 5

  • 1 University of Khorfakkan, UAE - (Walaa.fouda@ukf.ac.ae)
  • 2 University of Khorfakkan, UAE - (asmaa.hegazy@ukf.ac.ae)
  • 3 Mohamed bin Zayed University for Humanities, UAE - (Najla.alnaqbi@mbzuh.ac.ae)
  • 4 College of Business Administration, American University of the Middle East, Kuwait - (ebru.kahveci@aum.edu.kw)
  • 5 Department of Computer Engineering, Cyprus International University, Nicosia, 99258, North Cyprus, Turkey - (eozbilge@ciu.edu.tr)
  • Doi: https://doi.org/10.54216/IJNS.260130

    Received: November 12, 2024 Revised: January 23, 2025 Accepted: February 21, 2025
    Abstract

    This research work examines the critical challenge of enhancing educational environments through social media feedback, often impeded by the very uncertainties and complexities offered by textual data. Existing approaches either may indulge in sentiment analysis or may take the approach of basic data mining; nevertheless, they seldom consider ambiguity, contextual subtlety, and dynamic interventions. We propose an entirely new framework using Hybrid Neutrosophic Decision Optimization (HNDO) and Neutrosophic Sentiment Fusion (NSF) with deep learning-for advanced feature extraction-and reinforcement learning-for adaptive intervention strategies, with Explainable AI (XAI) for transparency. Presenting a new Neutrosophic Quantum Squirrel-Whale Decision Optimization (NQSWDO) framework to optimize educational enhancements based on feedback surveys and social media sentiment analysis, where it can collect, preprocess, extract features, fuse sentiments, optimize decisions, and detect concerns through reinforcement learning before interpreting feedbacks. A Neutrosophic Sentiment Fusion (NSF) model is applied to bring improvement into the accuracy of sentiment classification. Further refinement of educational improvements will come through the new application of hybrid neutrosophic decision optimization (HNDO), which incorporates multi-criteria decision analysis (MCDA) and fuzzy logic. For identification of key concerns, the VGG-Darknet detection model will be used, as well as a deep Q-network (DQN)-based reinforcement-learning system that dynamically intervenes in topic analysis. The last phase will comprehensively interpret feedback and adopt decision-making strategies to avoid wasting time in properly formulating useful educational policies. The results from the experiments indicate the practicality of the proposed framework for improving education decision-making through advanced methodologies on sentiment analysis, optimization, and reinforcement learning.

    Keywords :

    Neutrosophic Logic , Educational Environments , Social Media Feedback , Hybrid Neutrosophic Decision Optimization (HNDO) , Neutrosophic Sentiment Fusion (NSF) , Quantum Optimization Algorithms , VGG-Darknet Detection Model , Explainable AI

    References

    [1]      J. Valverde-Berrocoso, M. R. Fernández-Sánchez, F. I. Revuelta Dominguez, and M. J. Sosa-Díaz, “The educational integration of digital technologies pre-COVID-19: Lessons for teacher education,” PLoS One, vol. 16, no. 8, p. e0256283, 2021.

    [2]      J. W. S. Rizki, “Social Media as Tools of Communication and Learning,” QALAMUNA: Jurnal Pendidikan, Sosial, Dan Agama, vol. 15, no. 1, pp. 391–404, 2023.

    [3]      M. Monteleone and A. Postiglione, “Some Brief Considerations on Computational Statistics Effectiveness and Appropriateness in Natural Language Processing Applications,” in Fuzzy Systems and Data Mining X, IOS Press, 2024, pp. 529–542.

    [4]      M. Z. Labuguen, “Enhancing LSTM performance in sentiment analysis through advanced data preprocessing and model optimization techniques,” 2025.

    [5]      M. F. Cruz, M. E. R. Vargas, K. A. Álvarez Cadena, and D. C. Ortiz Delgado, “Studying health and inclusive education: sentiment analysis using neutrosophy as a research tool,” Neutrosophic Sets and Systems, vol. 42, no. 1, p. 20, 2021.

    [6]      O. A. Hassen, S. Mashhadani, I. Alhakam, and S. M. Darwish, “A New Paradigm for Decision Making under Uncertainty in Signature Forensics Applications based on Neutrosophic Rule Engine,” International Journal of Neutrosophic Science (IJNS), vol. 24, no. 2, 2024.

    [7]      Y. Chen and Z. Dong, “Students’ psychological analysis for classroom teaching strategies of art songs based on STEAM education,” Sustainability, vol. 16, no. 1, p. 323, 2024.

    [8]      D. Baidoo-Anu and L. O. Ansah, “Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning,” Journal of AI, vol. 7, no. 1, pp. 52–62, 2023.

    [9]      M. Mujahid et al., “Sentiment analysis and topic modeling on tweets about online education during COVID-19,” Applied Sciences, vol. 11, no. 18, p. 8438, 2021.

    [10]   M. Alzyoudi, N. Moussa, K. Almazroui, and S. Alnuaimi, “Analyzing Digital Education using Neutrosophic Sets,” International Journal of Neutrosophic Science (IJNS), vol. 20, no. 2, 2023.

    [11]   D. Smith-Mutegi, Y. Mamo, J. Kim, H. Crompton, and M. McConnell, “Perceptions of STEM education and artificial intelligence: a Twitter (X) sentiment analysis,” International Journal of STEM Education, vol. 12, no. 1, pp. 1–18, 2025.

    [12]   C. Xing, “Management model of higher education based on innovative using fuzzy sets,” Journal of Fuzzy Extension and Applications, vol. 5, no. 3, pp. 469–493, 2024.

    [13]   A. Jain, G. Jain, and D. Tewari, “KNetwork: advancing cross-lingual sentiment analysis for enhanced decision-making in linguistically diverse environments,” Knowledge and Information Systems, vol. 66, no. 5, pp. 2925–2943, 2024.

    [14]   J. Han, G. Liu, and Y. Yang, “Latent Dirichlet Allocation-Based Topic Mining Analysis of Educational Scientific Research Projects Based on 2360 NSF Education Projects,” TEM Journal, vol. 12, no. 2, 2023.

    [15]   O. A. Hassen, S. Mashhadani, I. Alhakam, and S. M. Darwish, “A New Paradigm for Decision Making under Uncertainty in Signature Forensics Applications based on Neutrosophic Rule Engine,” International Journal of Neutrosophic Science (IJNS), vol. 24, no. 2, 2024.

    [16]   N. M. Alnaqbi and W. Fouda, “Exploring the Role of ChatGPT and social media in Enhancing Student Evaluation of Teaching Styles in Higher Education Using Neutrosophic Sets,” International Journal of Neutrosophic Science (IJNS), vol. 20, no. 4, 2023.

    [17]   I. Awajan, M. Mohamad, and A. Al-Quran, “Sentiment analysis technique and neutrosophic set theory for mining and ranking big data from online reviews,” IEEE Access, vol. 9, pp. 47338–47353, 2021.

    [18]   S. Ahmadi, Z. Nourmohamadzadeh, and B. Amiri, “A hybrid DEMATEL and social network analysis model to identify factors affecting learners' satisfaction with MOOCs,” Heliyon, vol. 9, no. 7, 2023.

    [19]   K. Shi, “An efficient Model for Satisfaction Evaluation of College Students' Online Ideological and Political Education with Single-Valued Neutrosophic Numbers,” Neutrosophic Sets and Systems, vol. 76, pp. 430–448, 2025.

    [20]   W. Zou et al., “Exploring the relationship between social presence and learners’ prestige in MOOC discussion forums using automated content analysis and social network analysis,” Computers in Human Behavior, vol. 115, p. 106582, 2021.

    [21]   I. Yasser, A. A. Abd El-Khalek, H. E. Khalid, A. A. Salama, and A. K. Essa, “Cloud Computing and Neutrosophic Logic: Navigating Uncertainty in Educational Administration,” Neutrosophic Sets and Systems, vol. 78, no. 1, p. 24, 2025.

    [22]   F. K. Khaiser, A. Saad, and C. Mason, “Sentiment analysis of students’ feedback on institutional facilities using text-based classification and natural language processing (NLP),” Journal of Language and Communication, vol. 10, no. 1, pp. 101–111, 2023.

    [23]    F. P. A. de Medeiros and A. S. Gomes, “An approach based on social network analysis to enhance social presence in a collaborative learning environment,” IEEE Transactions on Education, vol. 65, no. 4, pp. 608–616, 2022.

    Cite This Article As :
    Fouda, Walaa. , Hegazy, Asmaa. , M., Najla. , Ozbilge, Ebru. , Özbilge, Emre. Enhancing educational environments with Social Media Feedback Evaluation Employing Hybrid Neutrosophic Decision Optimization (HNDO) and Neutrosophic Sentiment Fusion (NSF). International Journal of Neutrosophic Science, vol. , no. , 2025, pp. 365-390. DOI: https://doi.org/10.54216/IJNS.260130
    Fouda, W. Hegazy, A. M., N. Ozbilge, E. Özbilge, E. (2025). Enhancing educational environments with Social Media Feedback Evaluation Employing Hybrid Neutrosophic Decision Optimization (HNDO) and Neutrosophic Sentiment Fusion (NSF). International Journal of Neutrosophic Science, (), 365-390. DOI: https://doi.org/10.54216/IJNS.260130
    Fouda, Walaa. Hegazy, Asmaa. M., Najla. Ozbilge, Ebru. Özbilge, Emre. Enhancing educational environments with Social Media Feedback Evaluation Employing Hybrid Neutrosophic Decision Optimization (HNDO) and Neutrosophic Sentiment Fusion (NSF). International Journal of Neutrosophic Science , no. (2025): 365-390. DOI: https://doi.org/10.54216/IJNS.260130
    Fouda, W. , Hegazy, A. , M., N. , Ozbilge, E. , Özbilge, E. (2025) . Enhancing educational environments with Social Media Feedback Evaluation Employing Hybrid Neutrosophic Decision Optimization (HNDO) and Neutrosophic Sentiment Fusion (NSF). International Journal of Neutrosophic Science , () , 365-390 . DOI: https://doi.org/10.54216/IJNS.260130
    Fouda W. , Hegazy A. , M. N. , Ozbilge E. , Özbilge E. [2025]. Enhancing educational environments with Social Media Feedback Evaluation Employing Hybrid Neutrosophic Decision Optimization (HNDO) and Neutrosophic Sentiment Fusion (NSF). International Journal of Neutrosophic Science. (): 365-390. DOI: https://doi.org/10.54216/IJNS.260130
    Fouda, W. Hegazy, A. M., N. Ozbilge, E. Özbilge, E. "Enhancing educational environments with Social Media Feedback Evaluation Employing Hybrid Neutrosophic Decision Optimization (HNDO) and Neutrosophic Sentiment Fusion (NSF)," International Journal of Neutrosophic Science, vol. , no. , pp. 365-390, 2025. DOI: https://doi.org/10.54216/IJNS.260130