Volume 25 , Issue 2 , PP: 313-324, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
M. Sivakumar 1 * , Abdul Rajak Rabıyathul Basarıya 2 , M. Senthil 3 * , Vetriselvi .T 4 , G. Raja 5 , R. Rajavarman 6
Doi: https://doi.org/10.54216/IJNS.250227
In this study, a new notion of m-polar neutrosophic set (MPNS) and m-polar neutrosophic topology is introduced. To achieve this goal, first, we explore numerous representations of the concept of MPNS and deliberate its definitive characteristics. Some operations on MPNS were established. A score function is proposed for comparing the MPN numbers (MPNNs). Next, an MPN topology is introduced and closure, frontier, interior, and exterior for MPNS are defined with representative examples. Depression is a popular mental health problem that disturbs a broad range of individuals worldwide. Generally, people who undergo from this attitude have problems like mood swings, low concentration, suicide, and dementia. A social media platform such as Twitter enables to interact and share videos and photos that express their moods. Hence, the studies on social media content present an overview of personal sentiments, such as depression. Research has been undertaken on depression recognition in English and less in Arabic. The recognition of depression from Arabic social media falls after owing to the lack of resources and techniques and the available difficulty of the Arabic language. This article presents a novel Applied Linguistics with m-Polar Neutrosophic Set Mood Change and Depression on Social Media (MPNS-MCDSM) technique on Arabic Text Analysis. To accomplish this, the MPNS-MCDSM method undertakes a data pre-processing stage to convert the input dataset into a beneficial format. In addition, the Glove word embedding method is applied to the feature extraction from the preprocessed dataset. For the classification process, the m-Polar Neutrosophic Set (MPNS) classifier can be applied. Finally, the Whale Optimization Algorithm (WOA) is applied for optimum adjustment of the hyperparameters related to the MPNS classifier. The simulation outcomes of the MPNS-MCDSM technique are verified on the benchmark dataset. The experimental result analysis of the MPNS-MCDSM technique shows its promising solution over other existing approaches.
Arabic Language , m-Polar Neutrosophic Set , Whale Optimization Algorithm , Neutrosophic Set , Intuitionistic Fuzzy Set , Word Embedding
[1] Doaa Nihad Tomma, L. A. A. Al-Swidi. "Necessary and Sufficient Conditions for a Stability of the Concepts of Stable Interior and Stable Exterior via Neutrosophic Crisp Sets." International Journal of Neutrosophic Science, Vol. 24, No. 1, 2024 ,PP. 87-93
[2] Mathews, P., Sebastian, L. and Thankachan, B., 2024. Neutrosophic Fuzzy Score Matrices: A Robust Framework for Advancing Medical Diagnostics. International Journal of Neutrosophic Science, 23(3), pp.08-8.
[3] R. Saarumathi, W. Ritha. (2024). A Legitimate Productive Repertoire Replica Betwixt Envirotech Outlay Towards Fragile Commodities Using Trapezoidal Neutrosophic Fuzzy Number. International Journal of Neutrosophic Science, 24 ( 1 ), 104-118
[4] Abobala, M., 2020. n-Cyclic Refined Neutrosophic Algebraic Systems Of Sub-Indeterminacies, An Application To Rings and Modules. International Journal of Neutrosophic Science, 12, pp.81-95.
[5] Abobala, M,. "Classical Homomorphisms Between Refined Neutrosophic Rings and Neutrosophic Rings", International Journal of Neutrosophic Science, Vol. 5, pp. 72-75, 2020.
[6] Uddin, A.H.; Bapery, D.; Arif, A.S.M. Depression analysis of bangla social media data using gated recurrent neural network. In Proceedings of the 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 3–5 May 2019.
[7] Pintelas, E.G.; Kotsilieris, T.; Livieris, I.E.; Pintelas, P. A review of machine learning prediction methods for anxiety disorders. In Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-Exclusion, Thessaloniki, Greece, 20–22 June 2018; pp. 8–15.
[8] Al Asad, N.; Pranto, M.A.M.; Afreen, S.; Islam, M.M. Depression detection by analyzing social media posts of user. In Proceedings of the 2019 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON), Dhaka, Bangladesh, 28–30 November 2019.
[9] Almouzini, S.; Alageel, A. Detecting arabic depressed users from Twitter data. Procedia Comput. Sci. 2019, 163, 257–265.
[10] Arora, P.; Arora, P. Mining twitter data for depression detection. In Proceedings of the 2019 International Conference on Signal Processing and Communication (ICSC), Noida, India, 7–9 March 2019.
[11] Abbas, M.A., Munir, K., Raza, A., Samee, N.A., Jamjoom, M.M. and Ullah, Z., 2024. Novel Transformer Based Contextualized Embedding and Probabilistic Features for Depression Detection from Social Media. IEEE Access.
[12] Alarfaj, A., Hakami, N.A. and Hosnimahmoud, H., 2023. Predicting Violence-Induced Stress in an Arabic Social Media Forum. Intelligent Automation and Soft Computing, 35(2), pp.1423-1439.
[13] Abdullah, M. and Negied, N., 2024. Detection and prediction of Future Mental disorder from Social Media Data using Machine Learning, Ensemble Learning, and Large Language Models. IEEE Access.
[14] Kumar, A., Kumari, J. and Pradhan, J., 2023. Explainable deep learning for mental health detection from English and Arabic social media posts. ACM Transactions on Asian and Low-Resource Language Information Processing.
[15] Rodela, R.R., Efty, F.T., Rahman, M. and Wajiha, S., 2024. Analyzing Schizophrenic-prone text from social media content: a novel approach through ML and NLP (Doctoral dissertation, Brac University).
[16] Talaat, F.M., El-Gendy, E.M., Saafan, M.M. and Gamel, S.A., 2023. Utilizing social media and machine learning for personality and emotion recognition using PERS. Neural Computing and Applications, 35(33), pp.23927-23941.
[17] Bendebane, L., Laboudi, Z., Saighi, A., Al-Tarawneh, H., Ouannas, A. and Grassi, G., 2023. A Multi-Class Deep Learning Approach for Early Detection of Depressive and Anxiety Disorders Using Twitter Data. Algorithms, 16(12), p.543.
[18] Hashmi, M.R., Riaz, M. and Smarandache, F., 2020. m-Polar neutrosophic topology with applications to multi-criteria decision-making in medical diagnosis and clustering analysis. International Journal of Fuzzy Systems, 22, pp.273-292.
[19] Chen, J., Li, J., Zheng, D., Zheng, Q., Zhang, J., Wu, M. and Liu, C., 2024. Prediction of Grain Porosity Based on WOA–BPNN and Grain Compression Experiment. Applied Sciences, 14(7), p.2960.
[20] Maghraby, A. and Ali, H., 2022. Modern Standard Arabic mood changing and depression dataset. Data in Brief, 41, p.107999.
