ItemArticle

Artificial Intelligence Based Recruitment Prediction and Sentiment Analysis for Enhanced HR Efficiency

Title
Artificial Intelligence Based Recruitment Prediction and Sentiment Analysis for Enhanced HR Efficiency
Creator
Tripathi, Mano Ashish; Komatiguntala, Dhanalakshmi; Moorthygari, Sree Lakshmi; Dadhabai, Sundari; Mishra, Amit; Bommisetti, Ravi Kumar
Description
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.
Source
Journal of Machine and Computing;Volume;5;Issue;3;pp.1852-1863
Date
01-01-2025
Publisher
AnaPub Publications
Subject
Classification Performance; Employee Attrition; Ensemble Learning; Natural Language Processing; Predictive Modelling; Sentiment Analysis; Workforce Analytics
Coverage
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
Rights
All Open Access; Hybrid Gold Open Access
Relation
ISSN: 27891801;
Format
online
Language
English
Type
Article

Citation

Tripathi, Mano Ashish; Komatiguntala, Dhanalakshmi; Moorthygari, Sree Lakshmi; Dadhabai, Sundari; Mishra, Amit; Bommisetti, Ravi Kumar, “Artificial Intelligence Based Recruitment Prediction and Sentiment Analysis for Enhanced HR Efficiency,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23686.

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