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N-Gram Feature Based Resume Classification Using Machine Learning

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Computational Intelligence in Communications and Business Analytics (CICBA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1579))

Abstract

Shortlisting the right resume for the job is a tedious task for a job recruiter. For each post, thousands of applicants send their resumes; among all, very few resumes fit for the published job recruitment. Finding suitable resumes from a huge pool of resumes manually is a time-consuming process. This research addresses the above issue by suggesting a machine learning-based automated resume classification model. The automated resume classification model helps to classify the resume into different categories. Many classification models are used during the model development and found that the random forest classifier provides the most promising outcomes. The random forest classifier achieved the highest micro precision, recall, F1-score, and an accuracy value of 0.99 for the best case with the combination of uni- and bi-gram features.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Information Technology, Surat, India

    Pradeep Kumar Roy

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

    Shivam Chahar

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  1. Pradeep Kumar Roy
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  2. Shivam Chahar
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Corresponding author

Correspondence to Pradeep Kumar Roy .

Editor information

Editors and Affiliations

  1. Assam University, Silchar, India

    Somnath Mukhopadhyay

  2. Assam University, Silchar, India

    Sunita Sarkar

  3. Visva-Bharati University, Santiniketan, West Bengal, India

    Paramartha Dutta

  4. University of Kalyani, Nadia, India

    Jyotsna Kumar Mandal

  5. Assam University, Silchar, India

    Sudipta Roy

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Roy, P.K., Chahar, S. (2022). N-Gram Feature Based Resume Classification Using Machine Learning. In: Mukhopadhyay, S., Sarkar, S., Dutta, P., Mandal, J.K., Roy, S. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2022. Communications in Computer and Information Science, vol 1579. Springer, Cham. https://doi.org/10.1007/978-3-031-10766-5_18

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  • DOI: https://doi.org/10.1007/978-3-031-10766-5_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10765-8

  • Online ISBN: 978-3-031-10766-5

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