Break Down Resumes into Sections to Extract Data and Perform Text Analysis using Python

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Published: Jun 12, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i6s.6945
Keywords:
Resume Parser, Text Analysis, text classification, python, regular expressions, tagging, parsing

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Arvind Kumar Sinha
Faculty of Computing and IT Usha Martin University Ranchi, India
Md. Amir Khusru Akhtar
Faculty of Computing and IT Usha Martin University Ranchi, India
Mohit Kumar
Department of Information Technology MIT Art, Design and Technology University Pune, India

Abstract

The objective of AI-based resume screening is to automate the screening process, and text, keyword, and named entity recognition extraction are critical. This paper discusses segmenting resumes in order to extract data and perform text analysis. The raw CV file has been imported, and the resume data cleaned to remove extra spaces, punctuation and stop words. To extract names from resumes, regular expressions are used. We have also used the spaCy library which is considered the most accurate natural language processing library. It includes already-trained models for entity recognition, parsing, and tagging. The experimental method is used with resume data sourced from Kaggle, and external Source (MTIS).

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How to Cite
Sinha, A. K. ., Akhtar, M. A. K. ., & Kumar, M. . (2023). Break Down Resumes into Sections to Extract Data and Perform Text Analysis using Python . International Journal on Recent and Innovation Trends in Computing and Communication, 11(6s), 391–400. https://doi.org/10.17762/ijritcc.v11i6s.6945
Issue
Vol. 11 No. 6s (2023): Advances in Computational Modeling and Simulation of Computing Systems
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Articles

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