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Reseach Article

Automating Resume Classification: Leveraging NLP and AI for Efficient Candidate Screening

by Akshata Upadhye
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 40
Year of Publication: 2023
Authors: Akshata Upadhye
10.5120/ijca2023923208

Akshata Upadhye . Automating Resume Classification: Leveraging NLP and AI for Efficient Candidate Screening. International Journal of Computer Applications. 185, 40 ( Nov 2023), 46-50. DOI=10.5120/ijca2023923208

@article{ 10.5120/ijca2023923208,
author = { Akshata Upadhye },
title = { Automating Resume Classification: Leveraging NLP and AI for Efficient Candidate Screening },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2023 },
volume = { 185 },
number = { 40 },
month = { Nov },
year = { 2023 },
issn = { 0975-8887 },
pages = { 46-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number40/32956-2023923208/ },
doi = { 10.5120/ijca2023923208 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:28:18.405784+05:30
%A Akshata Upadhye
%T Automating Resume Classification: Leveraging NLP and AI for Efficient Candidate Screening
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 40
%P 46-50
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the growth in the number of applications received for a job posting, it has become a time-consuming process for the recruiters and the HR teams to review every individual resume. On the other hand, technological advancements in the field of natural language processing and AI can help build efficient automated tools to process and classify the resumes into relevant categories to help speed up the initial review process. In this paper various text processing techniques that will help generate representations of resumes suitable to be used by various machine learning algorithms and various types of classifiers are explored. A Word2vec word embedding model is trained and used to generate resume level embeddings. Finally, the performance of various classifiers is evaluated, and the best performing classifier is used for classifying resumes. The effectiveness of the resume classifier developed using the proposed approach is demonstrated using several metrics such as precision, recall and F1 score.

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Index Terms

Computer Science
Information Sciences

Keywords

Candidate Screening Resume Screening Word Embeddings Classifiers Machine Learning Natural Language Processing.