{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T12:02:02Z","timestamp":1784635322721,"version":"3.55.0"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MTI"],"abstract":"<jats:p>The early detection of psycho-emotional risks remains challenging despite its growing importance. Most existing models rely on a single data type, mainly questionnaires or text, and operate as \u201cblack boxes\u201d, limiting practical use. Psycho-emotional states are multidimensional, reflected in textual, structured, and temporal digital signals. Ignoring this complexity may result in information loss and lower prediction accuracy. This paper presents an explainable text-based computational framework for psycho-emotional risk classification using public datasets. Record-level structured information was used only when it was available within the same original observation and was not created by matching records across datasets. The study did not integrate observations across datasets or perform cross-dataset fusion; these aspects are considered potential directions for future research. Implemented in Python using PyTorch, the model was evaluated on two open-text datasets. The BERT-based configuration achieved accuracy of 0.593 \u00b1 0.009 and an ROC-AUC of 0.860 \u00b1 0.006, while RoBERTa-base improved the performance to 0.666 \u00b1 0.005 accuracy and a 0.896 \u00b1 0.004 ROC-AUC under five-fold cross-validation. The results demonstrate that classification quality depends on encoder selection, preprocessing, dataset characteristics, duplicate handling, calibration, and the way that available record-level information is represented. Therefore, the findings should be interpreted as record-level computational classification results rather than the clinical validation of psycho-emotional risk assessment.<\/jats:p>","DOI":"10.3390\/mti10070076","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T10:35:52Z","timestamp":1784630152000},"page":"76","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Text-Based Computational Framework for Psycho-Emotional Risk Classification: Calibration, Interpretability, and Encoder Comparison on Public Datasets"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4349-121X","authenticated-orcid":false,"given":"Orazmukhamed","family":"Bekmurat","sequence":"first","affiliation":[{"name":"Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vassiliy","family":"Serbin","sequence":"additional","affiliation":[{"name":"Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aliya","family":"Aizhanova","sequence":"additional","affiliation":[{"name":"Department of Smart Technologies in Engineering, International Engineering and Technological University, Almaty 050060, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nurdaulet","family":"Niyaz","sequence":"additional","affiliation":[{"name":"Department of Smart Technologies in Engineering, International Engineering and Technological University, Almaty 050060, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2232-2911","authenticated-orcid":false,"given":"Darkhan","family":"Yerezhep","sequence":"additional","affiliation":[{"name":"Technology Commercialization Center, Almaty Management University, Almaty 050060, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3333-5830","authenticated-orcid":false,"given":"Kanibek","family":"Sansyzbay","sequence":"additional","affiliation":[{"name":"Department of Radio Engineering, Electronics and Telecommunications, International Information Technologies University, Almaty 050060, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayaulym","family":"Oralbekova","sequence":"additional","affiliation":[{"name":"Department of Automation and Electric Power Engineering in Transport, International University of Transportation and Humanities, Almaty 050063, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malika","family":"Sagitzhanova","sequence":"additional","affiliation":[{"name":"Institute of Automation and Information Technologies, Satbayev University, Almaty 050013, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37282","DOI":"10.1038\/s41598-025-21301-1","article-title":"Artificial intelligence for predicting depression, anxiety and stress using psychometric data","volume":"15","author":"ShamsEldin","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1186\/s41043-025-01095-8","article-title":"Prevalence, associated factors, and machine learning-based prediction of depression, anxiety, and stress among university students: A cross-sectional study from Bangladesh","volume":"44","author":"Hasan","year":"2025","journal-title":"J. Health Popul. Nutr."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Taskynbayeva, M., and Gutoreva, A. (2025). Machine learning approaches to anxiety detection: Trends, model evaluation, and future directions. Front. Artif. Intell., 8.","DOI":"10.3389\/frai.2025.1630047"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e53714","DOI":"10.2196\/53714","article-title":"Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review","volume":"11","author":"Razavi","year":"2024","journal-title":"JMIR Ment. Health"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1186\/s41043-021-00276-5","article-title":"Prevalence and predicting factors of perceived stress among Bangladeshi university students using machine learning algorithms","volume":"40","author":"Rois","year":"2021","journal-title":"J. Health Popul. Nutr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e32736","DOI":"10.2196\/32736","article-title":"Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis","volume":"6","author":"Qasrawi","year":"2022","journal-title":"JMIR Form. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kargarandehkordi, A., Li, S., Lin, K., Phillips, K.T., Benzo, R.M., and Washington, P. (2025). Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review. Biosensors, 15.","DOI":"10.3390\/bios15040202"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jin, K., Rubio-Solis, A., Naik, R., Leff, D., Kinross, J., and Mylonas, G. (2025). Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains. Sensors, 25.","DOI":"10.3390\/s25134207"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ullah, A., Zhang, F., Song, Z., Wang, Y., Zhao, S., Riaz, W., and Li, G. (2024). Surface Electromyography-Based Recognition of Electronic Taste Sensations. Biosensors, 14.","DOI":"10.3390\/bios14080396"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Riaz, W., Ji, J., and Ullah, A. (2025). TriViT-Lite: A Compact Vision Transformer\u2013MobileNet Model with Texture-Aware Attention for Real-Time Facial Emotion Recognition in Healthcare. Electronics, 14.","DOI":"10.3390\/electronics14163256"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Riaz, W., Ji, J., Zaman, K., and Zengkang, G. (2025). Neural Network-Based Emotion Classification in Medical Robotics: Anticipating Enhanced Human\u2013Robot Interaction in Healthcare. Electronics, 14.","DOI":"10.3390\/electronics14071320"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Riaz, W., Ullah, A., and Ji, J. (2025). Multi-Scale Attention Networks with Feature Refinement for Medical Item Classification in Intelligent Healthcare Systems. Sensors, 25.","DOI":"10.3390\/s25175305"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Khoo, L.S., Lim, M.K., Chong, C.Y., and McNaney, R. (2024). Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. Sensors, 24.","DOI":"10.3390\/s24020348"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pinge, A., Gad, V., Jaisighani, D., Ghosh, S., and Sen, S. (2024). Detection and monitoring of stress using wearables: A systematic review. Front. Comput. Sci., 6.","DOI":"10.3389\/fcomp.2024.1478851"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, C.-M., and Lee, Y.-C. (2024). Affective Computing Using an Emotion-Perception System for Interactive Experiences with Multi-Sensing Interfaces. Preprints, 2024082106.","DOI":"10.20944\/preprints202408.2106.v1"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yu, M., Xu, H., Zhou, F., Xu, S., and Yin, H. (2023). A Deep-Learning-Based Multimodal Data Fusion Framework for Urban Region Function Recognition. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12120468"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mamieva, D., Abdusalomov, A.B., Kutlimuratov, A., Muminov, B., and Whangbo, T.K. (2023). Multimodal Emotion Detection via Attention-Based Fusion of Extracted Facial and Speech Features. Sensors, 23.","DOI":"10.3390\/s23125475"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mimikou, C., Kokkotis, C., Tsiptsios, D., Tsamakis, K., Savvidou, S., Modig, L., Christidi, F., Kaltsatou, A., Doskas, T., and Mueller, C. (2025). Explainable Machine Learning in the Prediction of Depression. Diagnostics, 15.","DOI":"10.20944\/preprints202505.0343.v1"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Haque, R., Islam, N., Islam, M., and Ahsan, M.M. (2022). A Comparative Analysis on Suicidal Ideation Detection Using NLP, Machine, and Deep Learning. Technologies, 10.","DOI":"10.3390\/technologies10030057"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115990","DOI":"10.1109\/ACCESS.2025.3585764","article-title":"Explainable AI for Sensor Signal Interpretation to Revolutionize Human Health Monitoring: A Review","volume":"13","author":"Alharthi","year":"2025","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.euroneuro.2024.08.005","article-title":"The challenges of using machine learning models in psychiatric research and clinical practice","volume":"88","author":"Ostojic","year":"2024","journal-title":"Eur. Neuropsychopharmacol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1080\/00273171.2025.2587379","article-title":"Neural Network Analysis of Psychological Data: A Step-by-Step Guide","volume":"61","author":"Tong","year":"2025","journal-title":"Multivar. Behav. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tathgir, A., Sharma, C.M., and Chariar, V.-M. (2024). EEG-Based Emotion Classification using Deep Learning: Approaches. Trends Bibliometr., 1\u201326.","DOI":"10.32388\/QIC0Z5"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1002\/jcad.12543","article-title":"Machine learning predictive models to guide prevention and intervention allocation for anxiety and depressive disorders among college students","volume":"103","author":"Zhai","year":"2025","journal-title":"J. Couns. Dev."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Schlicher, M., Li, Y., Murthy, S.M.K., Sun, Q., and Schuller, B.W. (2025). Emotionally adaptive support: A narrative review of affective computing for mental health. Front. Digit. Health, 7.","DOI":"10.3389\/fdgth.2025.1657031"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Farsadaki, V., and Griy-Brown, C. (2026). AI affective computing and behavioral health. Front. Comput. Sci., 7.","DOI":"10.3389\/fcomp.2025.1692728"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Palacio, M.-I., Bikki, T., Toledo, C., Ouyang, Y., Li, Z., Wang, Z., Toledo, F., Zeng, H., and Herrero, M.-T. (2025). Machine Learning, Physiological Signals, and Emotional Stress\/Anxiety: Pitfalls and Challenges. Appl. Sci., 15.","DOI":"10.3390\/app152111777"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Han, E.-G., Kang, T.-K., and Lim, M.-T. (2023). Physiological Signal-Based Real-Time Emotion Recognition Based on Exploiting Mutual Information with Physiologically Common Features. Electronics, 12.","DOI":"10.3390\/electronics12132933"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"97","DOI":"10.17816\/DD634885","article-title":"Prospects of machine learning applications in affective disorders","volume":"6","author":"Mosolova","year":"2025","journal-title":"Digit. Diagn."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"193229","DOI":"10.1109\/ACCESS.2024.3521219","article-title":"Development of an Algorithm for a National Microprocessor-Based Centralization System with a Modular Architecture KZ-MPC-MA Featuring Advanced Intelligent Control Functions","volume":"12","author":"Sansyzbay","year":"2024","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Babenko, T., Kolesnikova, K., Bakhtiyarova, Y., Yeskendirova, D., Sansyzbay, K., Sysoyev, A., and Kruchinin, O. (2026). Hybrid GNN\u2013LSTM Architecture for Probabilistic IoT Botnet Detection with Calibrated Risk Assessment. Computers, 15.","DOI":"10.3390\/computers15010026"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100350","DOI":"10.1016\/j.health.2024.100350","article-title":"A comprehensive review of predictive analytics models for mental illness using machine learning algorithms","volume":"6","author":"Islam","year":"2024","journal-title":"Healthc. Anal."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e13239","DOI":"10.1111\/exsy.13239","article-title":"Stress recognition with multi-modal sensing using bootstrapped ensemble deep learning model","volume":"40","author":"Singh","year":"2023","journal-title":"Expert. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Uria-Rivas, R., Rodriguez-Sanchez, M.C., Santos, O.C., Vaquero, J., and Boticario, J.G. (2019). Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios. Sensors, 19.","DOI":"10.3390\/s19204520"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Palko, D., Babenko, T., Bigdan, A., Kiktev, N., Hutsol, T., Kubo\u0144, M., Hnatiienko, H., Tabor, S., Gorbovy, O., and Borusiewicz, A. (2023). Cyber Security Risk Modeling in Distributed Information Systems. Appl. Sci., 13.","DOI":"10.3390\/app13042393"},{"key":"ref_36","unstructured":"Swain, V.D., Gao, L., Mondal, A., Abowd, G.D., and De Choudhury, M. (2024). Sensible and Sensitive AI for Worker Wellbeing: Factors that Inform Adoption and Resistance for Information Workers. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI \u201824), Honolulu, HI, USA, 11\u201316 May 2024, ACM."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s11257-017-9188-z","article-title":"Affective learning: Improving engagement and enhancing learning with affect-aware feedback","volume":"27","author":"Grawemeyer","year":"2017","journal-title":"User Model. User-Adapt. Interact."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lieskovsk\u00e1, E., Jakubec, M., Jarina, R., and Chmul\u00edk, M. (2021). A Review on Speech Emotion Recognition Using Deep Learning and Attention Mechanism. Electronics, 10.","DOI":"10.3390\/electronics10101163"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.knosys.2014.05.005","article-title":"Sentic patterns: Dependency-based rules for concept-level sentiment analysis","volume":"69","author":"Poria","year":"2014","journal-title":"Knowl.-Based Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.imavis.2012.06.016","article-title":"Categorical and dimensional affect analysis in continuous input: Current trends and future directions","volume":"31","author":"Gunes","year":"2013","journal-title":"Image Vis. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1109\/JSTSP.2017.2764438","article-title":"End-to-End Multimodal Emotion Recognition Using Deep Neural Networks","volume":"11","author":"Tzirakis","year":"2017","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2018.04.005","article-title":"Deep learning for healthcare applications based on physiological signals: A review","volume":"161","author":"Faust","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"432","DOI":"10.47839\/ijc.23.3.3662","article-title":"The Impact of Education Management Digitalisation on the Quality of Student Learning","volume":"23","author":"Zunimova","year":"2024","journal-title":"Int. J. Comput."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bekmurat, O., Akpanbetov, D., Tursynkhan, A., Demeubayeva, L., Duisenbekkyzy, Z., Sansyzbay, K., Kadirkulov, S., and Bakhtiyarova, Y. (2026). Explainable and Computationally Efficient NLP Framework for Detecting Psycho-Emotional Risk Signals in Social Media. Computers, 15.","DOI":"10.3390\/computers15050327"},{"key":"ref_45","first-page":"83","article-title":"Machine Learning models for the Cognitive Stress Detection Using Heart Rate Variability Signals","volume":"24","author":"Izzah","year":"2022","journal-title":"J. Tek. Ind."},{"key":"ref_46","first-page":"6280","article-title":"Hyperspectral image compression algorithms for phytosanitary inspection of agricultural crops in aerospace photography","volume":"99","author":"Sarinova","year":"2021","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.28991\/HIJ-2025-06-04-014","article-title":"Predicting Adolescent Suicide Risk in Smart Cities: An AI-Driven, Privacy-Preserving Architecture","volume":"6","author":"Aliaskarov","year":"2025","journal-title":"HighTech Innov. J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.28991\/HIJ-2025-06-03-016","article-title":"Mathematical Approaches and Algorithms in Big Data Architecture and Hybrid System Efficiency","volume":"6","author":"Aliaskarov","year":"2025","journal-title":"HighTech Innov. J."}],"container-title":["Multimodal Technologies and Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2414-4088\/10\/7\/76\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T11:17:14Z","timestamp":1784632634000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2414-4088\/10\/7\/76"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,21]]},"references-count":48,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["mti10070076"],"URL":"https:\/\/doi.org\/10.3390\/mti10070076","relation":{},"ISSN":["2414-4088"],"issn-type":[{"value":"2414-4088","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,21]]}}}