Automated Resume Parsing and Job Domain Prediction using Machine Learning

Arvind Kumar Sinha; Md Amir Khusru Akhtar; Mohit Kumar

doi:10.17485/IJST/v16i26.880

Article

Automated Resume Parsing and Job Domain Prediction using Machine Learning

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i26.880

Year: 2023, Volume: 16, Issue: 26, Pages: 1967-1974

Original Article

Automated Resume Parsing and Job Domain Prediction using Machine Learning

Arvind Kumar Sinha¹, Md Amir Khusru Akhtar¹, Mohit Kumar^2*

¹Faculty of Computing and Information Technology, Usha Martin University, Ranchi, India
²Department of IT, MIT Art Design and Technology University, Pune, India

*Corresponding Author
Email: [email protected]

Received Date:14 April 2023, Accepted Date:17 June 2023, Published Date:04 July 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: This study aims to develop an efficient approach for parsing resumes and predicting job domains using natural language processing (NLP) techniques and named entity recognition to enhance the resume screening process for recruiters. Methods: The proposed approach involves preprocessing steps, such as cleaning, tokenization, stop-word removal, stemming, and lemmatization, implemented with the PyMuPDF and doc2text Python modules. Regular expressions and the spaCy library are utilized for entity recognition and name extraction. The model achieved a prediction accuracy of 92.08% and an F1-score of 0.92 on a dataset of 1000 resumes. An ablation experiment assessed the contributions of different factors. Findings: The approach demonstrated a high prediction accuracy of 92.08% and F1-score of 0.92 for job domain prediction, effectively identifying relevant job domains from resumes. Evaluations on individual job domains showed excellent precision and recall scores, validating its applicability. Preprocessing techniques significantly improved accuracy, while the integration of regular expressions and spaCy enhanced the model’s performance. This approach automates resume screening, reducing recruiters’ workload, saving time and effort, and improving candidate selection and the hiring process. Novelty: This study introduces a novel approach combining NLP techniques, regular expressions, and entity recognition for resume parsing and job domain prediction. This integration enhances accuracy and efficiency, offering a unique solution for resume screening.

Keywords: Resume parsing; Job domain prediction; Entity recognition; Machine learning; Natural Language Processing

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Copyright

© 2023 Sinha et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)