{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T23:02:57Z","timestamp":1784674977613,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The timely detection of psycho-emotional risks has become increasingly important due to the rapid growth of social media platforms. This study examines user-generated text as a potential source of early indicators of psychological vulnerability. The proposed NLP-based framework incorporates behavioral features to improve the interpretation of users\u2019 psycho-emotional states. In addition to text classification, the study considers structured behavioral indicators to support psycho-emotional risk analysis. Particular attention is given to interpretability. SHAP-based techniques are applied to reveal the contribution of individual features and to provide a clearer explanation of model predictions. The evaluation was conducted on publicly available datasets containing textual data and aggregated behavioral\/physiological indicators. No raw physiological streams, wearable sensor data, or biometric recordings were used. The two datasets were employed in complementary experimental settings and were not aligned at the individual-sample level; accordingly, the broader analytical perspective explored in this study should not be interpreted as a single end-to-end or fully aligned multimodal learning framework. The proposed BERT-based model with SHAP interpretability achieved an accuracy of 96.3%, an F1-score of 0.96, and a ROC\u2013AUC score of 0.98, showing consistent improvement over baseline models, including Random Forests and Support Vector Machines.<\/jats:p>","DOI":"10.3390\/computers15050327","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T13:53:24Z","timestamp":1779371604000},"page":"327","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Explainable and Computationally Efficient NLP Framework for Detecting Psycho-Emotional Risk Signals in Social Media"],"prefix":"10.3390","volume":"15","author":[{"given":"Orazmukhamed","family":"Bekmurat","sequence":"first","affiliation":[{"name":"Department of Cybersecurity, Information Processing and Storage, Satbayev University, Almaty 050000, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Darkhan","family":"Akpanbetov","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\/0000-0003-1332-3936","authenticated-orcid":false,"given":"Ainur","family":"Tursynkhan","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, International Engineering and Technological University, Almaty 050060, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9183-2212","authenticated-orcid":false,"given":"Laura","family":"Demeubayeva","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, International Engineering and Technological University, Almaty 050060, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7743-0795","authenticated-orcid":false,"given":"Zhansaya","family":"Duisenbekkyzy","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kanibek","family":"Sansyzbay","sequence":"additional","affiliation":[{"name":"Department of Radio Engineering, Electronics and Telecommunications, International Information Technologies University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shingis","family":"Kadirkulov","sequence":"additional","affiliation":[{"name":"Department of the Educational and Methodological Directorate, Sagadat Nurmagambetov Military Institute of the Ground Forces, Almaty 050030, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8735-7683","authenticated-orcid":false,"given":"Yelena","family":"Bakhtiyarova","sequence":"additional","affiliation":[{"name":"Department of Radio Engineering, Electronics and Telecommunications, International Information Technologies University, Almaty 050040, Kazakhstan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1016\/j.procs.2022.12.073","article-title":"Suicide Ideation Detection on Social Networks: Short Literature Review","volume":"215","author":"Lasria","year":"2022","journal-title":"Procedia Comput. 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