Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Tripathi, Mano Ashish; Komatiguntala, Dhanalakshmi; Moorthygari, Sree Lakshmi; Dadhabai, Sundari; Mishra, Amit; Bommisetti, Ravi Kumar Title A name given to the resource Artificial Intelligence Based Recruitment Prediction and Sentiment Analysis for Enhanced HR Efficiency Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Machine and Computing;Volume;5;Issue;3;pp.1852-1863 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.53759/7669/jmc202505145" target="_blank" rel="noreferrer noopener">https://doi.org/10.53759/7669/jmc202505145</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010143052?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010143052?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Tripathi M.A., School of Management Studies, Motilal Nehru National Institute of Technology Allahabad, Uttar Pradesh, Prayagraj, India; Komatiguntala D., School of Business and Management, Christ University, Yeswanthpur Campus, Bangalore, India; Moorthygari S.L., Department of Business Management, Mahatma Gandhi University, Telangana, Nalgonda, India; Dadhabai S., KL Business School, Koneru Lakshmaiah Educational Foundation, Andhra Pradesh, Vaddeswaram, India; Mishra A., Department of Computer Science and Applications, Dr. Vishwanath Karad, MITWPU, Maharashtra, Pune, India; Bommisetti R.K., Andhra Pradesh, India Description An account of the resource In the present era of data-driven organizational environment, the practice of Human Resource Management (HRM) has become increasingly reliant on intelligent Decision-Support Systems (DSS). This study develops a multifaceted two-pipeline model of Predictive Modelling (PM) and Sentiment Analysis (SA) to enhance workforce analytics capabilities. A publicly available HRM analytic dataset is used to train supervised classification models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM), as well as an ensemble model that integrates these classifiers. These approaches use structured data to predict employee attrition based on features such as age, job role, experience, and job satisfaction. The unstructured textual data sources, including resumes and employee reviews, are handled using state-of-the-art Natural Language Processing (NLP) such as tokenization, Term Frequency-Inverse Document Frequency (TF-IDF), and Bidirectional Encoder Representations as Transformers (BERT)-based embeddings. The new Mathematically Modified Robustly Optimized BERT Pretraining (MM-RoBERTa) is proposed for extracting the PM and SA. All the models are evaluated using k-fold Cross-Validation (CV) and standard evaluation measures, namely Accuracy, F1-score, Area Under the Receiver Operating Characteristic Curve (AUC), and Mean Absolute Error (MAE). The ensemble model achieves a predictive accuracy of 91.3%, and MM-RoBERTa outperforms existing SA with an accuracy of 93.1 %. The combination of predictive and affective insights is of practical use in fine-tuning talent retention, empowering HRM professionals to make informed decisions based on objective performance indicators and subjective emotional states. 2025 The Authors. Subject The topic of the resource Classification Performance; Employee Attrition; Ensemble Learning; Natural Language Processing; Predictive Modelling; Sentiment Analysis; Workforce Analytics Publisher An entity responsible for making the resource available AnaPub Publications Relation A related resource ISSN: 27891801; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online