An Improved Algorithm for Recruitment Text Categorization

Hui Zhao; Xin Liu; Wenjie Guo; Keke Gai; Ying Wang

doi:10.1007/978-981-15-1922-2_24

An Improved Algorithm for Recruitment Text Categorization

Hui Zhao, Xin Liu, Wenjie Guo, Keke Gai, Ying Wang^*

^*Corresponding author for this work

School of Cyberspace Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

With the rapid development of the Internet, online recruitment has gradually become a mainstream. In the process of obtaining the text of recruitment information, a large volume of texts are not part of recruitment information. Currently, common text categorization algorithms include k-Nearest Neighbor, Support Vector Machine (SVM) and Naive Bayes. In addition, there are numerous related technical terms in the recruitment information, which affects the accuracy of the ordinary Bayesian text categorization algorithm. However, there is not uniform format for the text information of recruitment. This paper improves the original Naive Bayes algorithm and proposes a Reinforcement Naive Bayes (R-NB) algorithm to enhance the accuracy of recruitment information categorization. Experiments have demonstrated that the improved algorithm has a higher categorization accuracy and practicability than the original algorithm.

Original language	English
Title of host publication	Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health - International 2019 Cyberspace Congress, CyberDI and CyberLife, Proceedings
Editors	Huansheng Ning
Publisher	Springer
Pages	335-348
Number of pages	14
ISBN (Print)	9789811519215
DOIs	https://doi.org/10.1007/978-981-15-1922-2_24
Publication status	Published - 2019
Event	3rd International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019 - Beijing, China Duration: 16 Dec 2019 → 18 Dec 2019

Publication series

Name	Communications in Computer and Information Science
Volume	1137 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	3rd International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019
Country/Territory	China
City	Beijing
Period	16/12/19 → 18/12/19

Keywords

Feature extraction
Naive Bayes algorithm
Recruitment categorization
Text categorization

Access to Document

10.1007/978-981-15-1922-2_24

Cite this

Zhao, H., Liu, X., Guo, W., Gai, K., & Wang, Y. (2019). An Improved Algorithm for Recruitment Text Categorization. In H. Ning (Ed.), Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health - International 2019 Cyberspace Congress, CyberDI and CyberLife, Proceedings (pp. 335-348). (Communications in Computer and Information Science; Vol. 1137 CCIS). Springer. https://doi.org/10.1007/978-981-15-1922-2_24

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title = "An Improved Algorithm for Recruitment Text Categorization",

abstract = "With the rapid development of the Internet, online recruitment has gradually become a mainstream. In the process of obtaining the text of recruitment information, a large volume of texts are not part of recruitment information. Currently, common text categorization algorithms include k-Nearest Neighbor, Support Vector Machine (SVM) and Naive Bayes. In addition, there are numerous related technical terms in the recruitment information, which affects the accuracy of the ordinary Bayesian text categorization algorithm. However, there is not uniform format for the text information of recruitment. This paper improves the original Naive Bayes algorithm and proposes a Reinforcement Naive Bayes (R-NB) algorithm to enhance the accuracy of recruitment information categorization. Experiments have demonstrated that the improved algorithm has a higher categorization accuracy and practicability than the original algorithm.",

keywords = "Feature extraction, Naive Bayes algorithm, Recruitment categorization, Text categorization",

author = "Hui Zhao and Xin Liu and Wenjie Guo and Keke Gai and Ying Wang",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Singapore Pte Ltd.; 3rd International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019 ; Conference date: 16-12-2019 Through 18-12-2019",

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doi = "10.1007/978-981-15-1922-2_24",

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series = "Communications in Computer and Information Science",

publisher = "Springer",

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address = "Germany",

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Zhao, H, Liu, X, Guo, W, Gai, K & Wang, Y 2019, An Improved Algorithm for Recruitment Text Categorization. in H Ning (ed.), Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health - International 2019 Cyberspace Congress, CyberDI and CyberLife, Proceedings. Communications in Computer and Information Science, vol. 1137 CCIS, Springer, pp. 335-348, 3rd International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019, Beijing, China, 16/12/19. https://doi.org/10.1007/978-981-15-1922-2_24

An Improved Algorithm for Recruitment Text Categorization. / Zhao, Hui; Liu, Xin; Guo, Wenjie et al.
Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health - International 2019 Cyberspace Congress, CyberDI and CyberLife, Proceedings. ed. / Huansheng Ning. Springer, 2019. p. 335-348 (Communications in Computer and Information Science; Vol. 1137 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Improved Algorithm for Recruitment Text Categorization

AU - Zhao, Hui

AU - Liu, Xin

AU - Guo, Wenjie

AU - Gai, Keke

AU - Wang, Ying

PY - 2019

Y1 - 2019

N2 - With the rapid development of the Internet, online recruitment has gradually become a mainstream. In the process of obtaining the text of recruitment information, a large volume of texts are not part of recruitment information. Currently, common text categorization algorithms include k-Nearest Neighbor, Support Vector Machine (SVM) and Naive Bayes. In addition, there are numerous related technical terms in the recruitment information, which affects the accuracy of the ordinary Bayesian text categorization algorithm. However, there is not uniform format for the text information of recruitment. This paper improves the original Naive Bayes algorithm and proposes a Reinforcement Naive Bayes (R-NB) algorithm to enhance the accuracy of recruitment information categorization. Experiments have demonstrated that the improved algorithm has a higher categorization accuracy and practicability than the original algorithm.

AB - With the rapid development of the Internet, online recruitment has gradually become a mainstream. In the process of obtaining the text of recruitment information, a large volume of texts are not part of recruitment information. Currently, common text categorization algorithms include k-Nearest Neighbor, Support Vector Machine (SVM) and Naive Bayes. In addition, there are numerous related technical terms in the recruitment information, which affects the accuracy of the ordinary Bayesian text categorization algorithm. However, there is not uniform format for the text information of recruitment. This paper improves the original Naive Bayes algorithm and proposes a Reinforcement Naive Bayes (R-NB) algorithm to enhance the accuracy of recruitment information categorization. Experiments have demonstrated that the improved algorithm has a higher categorization accuracy and practicability than the original algorithm.

KW - Feature extraction

KW - Naive Bayes algorithm

KW - Recruitment categorization

KW - Text categorization

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U2 - 10.1007/978-981-15-1922-2_24

DO - 10.1007/978-981-15-1922-2_24

M3 - Conference contribution

AN - SCOPUS:85076966045

SN - 9789811519215

T3 - Communications in Computer and Information Science

SP - 335

EP - 348

BT - Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health - International 2019 Cyberspace Congress, CyberDI and CyberLife, Proceedings

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T2 - 3rd International Conference on Cyberspace Data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

Y2 - 16 December 2019 through 18 December 2019

ER -

Zhao H, Liu X, Guo W, Gai K, Wang Y. An Improved Algorithm for Recruitment Text Categorization. In Ning H, editor, Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health - International 2019 Cyberspace Congress, CyberDI and CyberLife, Proceedings. Springer. 2019. p. 335-348. (Communications in Computer and Information Science). doi: 10.1007/978-981-15-1922-2_24

An Improved Algorithm for Recruitment Text Categorization

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