TY - GEN
T1 - Comparison of Job Titles for Specific Terms
T2 - 24th International Conference on Information Integration and Web Intelligence, iiWAS 2022, held in conjunction with the 20th International Conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM 2022
AU - Alsudais, Abdulkareem
AU - Aldumaykhi, Abdullah
AU - Otai, Saad
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The ability to analyze a single term or phrase and generate its most relevant job titles and their similarities can be beneficial for organizations and government agencies. In this paper, we propose a framework that relies on a corpus of job postings for a single term and utilizes several text mining techniques to discover insights. The main outcome resulting from the application of our framework is a matrix and clusters representing the textual similarities between the job titles. To trial our framework, we studied the term “data science” and collected a corpus that consisted of 9,439 online job postings that contained the term. Our analysis identified 12 job titles and compared their similarities, allowing us to posit several important conclusions for data science and related fields.
AB - The ability to analyze a single term or phrase and generate its most relevant job titles and their similarities can be beneficial for organizations and government agencies. In this paper, we propose a framework that relies on a corpus of job postings for a single term and utilizes several text mining techniques to discover insights. The main outcome resulting from the application of our framework is a matrix and clusters representing the textual similarities between the job titles. To trial our framework, we studied the term “data science” and collected a corpus that consisted of 9,439 online job postings that contained the term. Our analysis identified 12 job titles and compared their similarities, allowing us to posit several important conclusions for data science and related fields.
KW - Data science
KW - Job postings
KW - Semantics
KW - Text similarity
UR - http://www.scopus.com/inward/record.url?scp=85145006119&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-21047-1_8
DO - 10.1007/978-3-031-21047-1_8
M3 - Conference contribution
AN - SCOPUS:85145006119
SN - 9783031210464
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 98
EP - 103
BT - Information Integration and Web Intelligence - 24th International Conference, iiWAS 2022, Proceedings
A2 - Pardede, Eric
A2 - Delir Haghighi, Pari
A2 - Khalil, Ismail
A2 - Kotsis, Gabriele
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 28 November 2022 through 30 November 2022
ER -