{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T06:31:22Z","timestamp":1772087482603,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T00:00:00Z","timestamp":1771632000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>In modern organizations, IT Service Management (ITSM) relies on the efficient handling of large volumes of unstructured textual data, such as support tickets and incident reports. This study investigates the automated classification of IT support requests as a data-driven decision-support task within a real-world enterprise ITSM context, addressing challenges posed by multilingual content and severe class imbalance. We propose an applied machine-learning and natural language processing (NLP) pipeline combining text cleaning, stratified data splitting, and supervised model training under realistic evaluation conditions. Multiple classification models were evaluated on historical enterprise ticket data, including a Logistic Regression baseline and transformer-based architectures (multilingual BERT and XLM-RoBERTa). Model validation distinguishes between deployment-oriented evaluation on naturally imbalanced data and diagnostic analysis using training-time class balancing to examine minority-class behavior. Results indicate that Logistic Regression performs reliably for high-frequency, well-defined request categories, while transformer-based models achieve consistently higher macro-averaged F1-scores and improved recognition of semantically complex and underrepresented classes. Training-time oversampling increases sensitivity to minority request types without improving overall accuracy on unbalanced test data, highlighting the importance of metric selection in ITSM evaluation. The findings provide an applied empirical comparison of established text-classification models in ITSM, incorporating both predictive performance and computational efficiency considerations, and offer practical guidance for supporting IT support agents during ticket triage and automated request classification.<\/jats:p>","DOI":"10.3390\/systems14020223","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T10:00:52Z","timestamp":1771840852000},"page":"223","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Based Classification of IT Support Requests in Enterprise Service Management Systems"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3954-3197","authenticated-orcid":false,"given":"Audrius","family":"Razma","sequence":"first","affiliation":[{"name":"Faculty of Marine Technology and Natural Sciences, Klaipeda University, 92294 Klaipeda, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7027-394X","authenticated-orcid":false,"given":"Robertas","family":"Jurkus","sequence":"additional","affiliation":[{"name":"Faculty of Marine Technology and Natural Sciences, Klaipeda University, 92294 Klaipeda, Lithuania"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"key":"ref_1","unstructured":"Limited, A. (2019). ITIL\u00ae Foundation, ITIL\u00ae 4 Edition, TSO (The Stationery Office). [1st ed.]."},{"key":"ref_2","unstructured":"ISACA (2018). COBIT\u00ae 2019 Framework: Governance and Management Objectives, ISACA. [1st ed.]."},{"key":"ref_3","unstructured":"(2018). 2018 Information Technology\u2014Service Management (Standard No. ISO\/IEC 20000-1)."},{"key":"ref_4","unstructured":"World Economic Forum (2025, December 10). The Future of Jobs Report 2025. Available online: https:\/\/www.weforum.org\/publications\/the-future-of-jobs-report-2025\/in-full\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.procs.2024.03.014","article-title":"Comparison of Effectiveness of Logistic Regression, Naive Bayes, and Random Forest Algorithms in Predicting Student Arguments","volume":"234","author":"Wahyuningsih","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201916, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"24039","DOI":"10.1038\/s41598-021-03430-5","article-title":"Research on expansion and classification of imbalanced data based on SMOTE algorithm","volume":"11","author":"Wang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, X., Yang, K., Cui, T., Chen, M., and He, L. (2022). A Study of Text Vectorization Method Combining Topic Model and Transfer Learning. Processes, 10.","DOI":"10.3390\/pr10020350"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"193380","DOI":"10.1109\/ACCESS.2020.3032840","article-title":"IT Ticket Classification: The Simpler, the Better","volume":"8","author":"Revina","year":"2020","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2780","DOI":"10.11591\/eei.v10i5.3157","article-title":"Performance comparison of TF-IDF and Word2Vec models for emotion text classification","volume":"10","author":"Cahyani","year":"2021","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_12","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the North American Chapter of the Association for Computational Linguistics, Minneapolis, MN, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Naseer, M., Asvial, M., and Sari, R.F. (2021, January 13\u201316). An Empirical Comparison of BERT, RoBERTa, and Electra for Fact Verification. Proceedings of the 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Jeju Island, Republic of Korea.","DOI":"10.1109\/ICAIIC51459.2021.9415192"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100359","DOI":"10.1016\/j.health.2024.100359","article-title":"Assessing the impact on quality of prediction and inference from balancing in multilevel logistic regression","volume":"6","author":"Hass","year":"2024","journal-title":"Healthc. Anal."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112677","DOI":"10.1016\/j.asoc.2024.112677","article-title":"Deep generative approaches for oversampling in imbalanced data classification problems: A comprehensive review and comparative analysis","volume":"170","author":"Moattar","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"112922","DOI":"10.1016\/j.asoc.2025.112922","article-title":"A robust ensemble classifier for imbalanced data via adaptive variety oversampling and embedded sampling rate","volume":"174","author":"Dou","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100473","DOI":"10.1016\/j.eij.2024.100473","article-title":"Email spam detection by deep learning models using novel feature selection technique and BERT","volume":"26","author":"Nasreen","year":"2024","journal-title":"Egypt. Inform. J."},{"key":"ref_18","first-page":"2165","article-title":"Advanced BERT and CNN-Based Computational Model for Phishing Detection in Enterprise Systems","volume":"141","author":"Gupta","year":"2024","journal-title":"CMES-Comput. Model. Eng. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100645","DOI":"10.1016\/j.eij.2025.100645","article-title":"Ransomware detection and family classification using fine-tuned BERT and RoBERTa models","volume":"30","author":"Hussain","year":"2025","journal-title":"Egypt. Inform. J."},{"key":"ref_20","first-page":"3395","article-title":"Comparative Analysis of Machine Learning Algorithms for Email Phishing Detection Using TF-IDF, Word2Vec, and BERT","volume":"81","author":"Tawil","year":"2024","journal-title":"Comput. Mater. Contin."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106688","DOI":"10.1016\/j.engappai.2023.106688","article-title":"Strategies for enhancing the performance of news article classification in Bangla: Handling imbalance and interpretation","volume":"125","author":"Hasib","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_22","unstructured":"Webber, B., Cohn, T., He, Y., and Liu, Y. (2020). From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 16\u201320 November 2020, Association for Computational Linguistics."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1016\/j.procs.2025.04.398","article-title":"Classification of Mental Illnesses from Reddit Posts Using Sentence-BERT Embeddings and Neural Networks","volume":"258","author":"Wagay","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"112939","DOI":"10.1016\/j.knosys.2024.112939","article-title":"Spam email classification based on cybersecurity potential risk using natural language processing","volume":"310","author":"Fidalgo","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"110478","DOI":"10.1016\/j.asoc.2023.110478","article-title":"Efficient e-mail spam filtering approach combining Logistic Regression model and Orthogonal Atomic Orbital Search algorithm","volume":"144","author":"Manita","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_26","unstructured":"Al-Turjman, F. (2024). 4\u2014Efficient spam email classification logistic regression model trained by modified social network search algorithm. Computational Intelligence and Blockchain in Complex Systems, Morgan Kaufmann. Advanced Studies in Complex Systems."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101852","DOI":"10.1016\/j.csl.2025.101852","article-title":"Mono- and cross-lingual evaluation of representation language models on less-resourced languages","volume":"95","author":"Armendariz","year":"2026","journal-title":"Comput. Speech Lang."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"118246","DOI":"10.1016\/j.eswa.2022.118246","article-title":"Cross lingual transfer learning for sentiment analysis of Italian TripAdvisor reviews","volume":"209","author":"Catelli","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_29","first-page":"8660828","article-title":"Short-Text Classification Detector: A BERT-Based Mental Approach","volume":"2022","author":"Hu","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Miller, C., Portlock, T., Nyaga, D.M., and O\u2019Sullivan, J.M. (2024). A review of model evaluation metrics for machine learning in genetics and genomics. Front. Bioinform., 4.","DOI":"10.3389\/fbinf.2024.1457619"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chicco, D., and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom., 21.","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"78368","DOI":"10.1109\/ACCESS.2021.3084050","article-title":"The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen\u2019s Kappa and Brier Score in Binary Classification Assessment","volume":"9","author":"Chicco","year":"2021","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.ipm.2013.08.006","article-title":"The impact of preprocessing on text classification","volume":"50","author":"Uysal","year":"2014","journal-title":"Inf. Process. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., and Zhang, Y. (2014). An Effective TF\/IDF-Based Text-to-Text Semantic Similarity Measure for Text Classification. Web Information Systems Engineering\u2014WISE, 15th International Conference, Thessaloniki, Greece, 12\u201314 October 2014, Springer.","DOI":"10.1007\/978-3-319-11749-2"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Moreo, A., Esuli, A., and Sebastiani, F. (2016). Distributional Random Oversampling for Imbalanced Text Classification. SIGIR \u201916: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy, 17\u201321 July 2016, Association for Computing Machinery.","DOI":"10.1145\/2911451.2914722"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mohammadi, S., and Chapon, M. (2020, January 14\u201316). Investigating the Performance of Fine-tuned Text Classification Models Based-on Bert. Proceedings of the 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS), Yanuca Island, Fiji.","DOI":"10.1109\/HPCC-SmartCity-DSS50907.2020.00162"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sun, M., Huang, X., Ji, H., Liu, Z., and Liu, Y. (2019). How to Fine-Tune BERT for Text Classification?. Chinese Computational Linguistics: 18th China National Conference, CCL 2019, Kunming, China, 18\u201320 October 2019, Springer.","DOI":"10.1007\/978-3-030-32381-3"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Eltahier, S., Dawood, O., and Saeed, I. (2025). BERT Fine-Tuning for Software Requirement Classification: Impact of Model Components and Dataset Size. 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