Abstract
With an increase in the number of online job posts, it is becoming increasingly challenging for both job searchers and employers to navigate this large quantity of information. Therefore, it is crucial to use natural language processing techniques to analyze and draw inferences from these job postings. This study focuses on the most recent job postings in Turkiye for Computer Engineering and Management Information Systems. The objective is to extract skills for the job postings for job seekers wanting to apply for a new position. LSA is a statistical method for figuring out the underlying characteristics and meanings of sentences and words in natural language. Frequency analysis was also performed in addition to the LSA analyses with the goal of conducting a thorough examination of job postings. This study was conducted to determine and evaluate the skills that the sector actually needs. Thus, job seekers will have the chance to develop themselves in a more planned way. The findings indicate that the job postings for the two departments reflect various characteristics in terms of social and technical abilities. A higher requirement for social skills is thought to exist in the field of Management Information Systems rather than Computer Engineering. It has been discovered that employment involving data have been highly popular in recent years, and both departments often list opportunities involving data analysis.
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References
Alsmadi, D., Omar, K.: Analyzing the needs of ICT job market in Jordan using a text mining approach. In: 2022 International Conference on Business Analytics for Technology and Security (ICBATS), pp. 1–5. IEEE (2022)
Amalia, A., Gunawan, D., Fithri, Y., Aulia, I.: Automated Bahasa Indonesia essay evaluation with latent semantic analysis. J. Phys. Conf. Ser. 1235, 012100 (2019)
Chen, J., et al.: Data analysis and knowledge discovery in web recruitment-based on big data related jobs. In: 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), pp. 142–146. IEEE (2019)
Chen, Y., Li, X., Zhang, S.: Structured latent factor analysis for large-scale data: Identifiability, estimability, and their implications. J. Am. Stat. Assoc. 115(532), 1756–1770 (2020)
Evangelopoulos, N.E.: Latent semantic analysis. Wiley Interdiscip. Rev. Cogn. Sci. 4(6), 683–692 (2013)
Han, K., Chien, W.T., Chiu, C., Cheng, Y.: Application of support vector machine (SVM) in the sentiment analysis of twitter dataset. Appl. Sci. 10, 1125 (2020). https://doi.org/10.3390/app10031125
Jiang, J., Ye, S., Wang, W., Xu, J., Luo, X.: Learning effective representations for person-job fit by feature fusion. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2549–2556 (2020)
Jirasatjanukul, K., Nilsook, P., Wannapiroon, P.: Intelligent human resource management using latent semantic analysis with the internet of things. Int. J. Comput. Theory Eng. 11, 23–26 (2019). https://doi.org/10.7763/ijcte.2019.v11.1235
Khaouja, I., Kassou, I., Ghogho, M.: A survey on skill identification from online job ads. IEEE Access 9, 118134–118153 (2021)
Luo, Y., Zhang, H., Wen, Y., Zhang, X.: ResumeGAN: an optimized deep representation learning framework for talent-job fit via adversarial learning. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1101–1110 (2019)
Maletic, J., Marcus, A.: Using latent semantic analysis to identify similarities in source code to support program understanding. In: Proceedings 12th IEEE Internationals Conference on Tools with Artificial Intelligence. ICTAI 2000, pp. 46–53 (2000). https://doi.org/10.1109/TAI.2000.889845
Malinowski, J., Keim, T., Wendt, O., Weitzel, T.: Matching people and jobs: a bilateral recommendation approach. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS 2006), vol. 6, p. 137c. IEEE (2006)
Oakman, J., Van Ameringen, M., Mancini, C., Farvolden, P.: A confirmatory factor analysis of a self-report version of the Liebowitz social anxiety scale. J. Clin. Psychol. 59(1), 149–161 (2003)
Olney, A.M.: Large-scale latent semantic analysis. Behav. Res. Methods 43(2), 414–423 (2011)
Piróg, D., Hibszer, A.: Utilising the potential of job postings for auditing learning outcomes and improving graduates’ chances on the labor market. Higher Educ. Q. (2023)
Qin, C., et al.: Enhancing person-job fit for talent recruitment: an ability-aware neural network approach. In: The 41st international ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 25–34 (2018)
Rofiq, R.A., et al.: Indonesian news extractive text summarization using latent semantic analysis. In: 2021 International Conference on Computer Science and Engineering (IC2SE), vol. 1, pp. 1–5. IEEE (2021)
Saidani, N., Adi, K., Allili, M.S.: A semantic-based classification approach for an enhanced spam detection. Comput. Secur. 94, 101716 (2020)
Salloum, S.A., Khan, R., Shaalan, K.: A survey of semantic analysis approaches. In: Hassanien, A.-E., Azar, A.T., Gaber, T., Oliva, D., Tolba, F.M. (eds.) AICV 2020. AISC, vol. 1153, pp. 61–70. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44289-7_6
Salm, V.: Student Success in Co-operative Education: An Analysis of Job Postings and Performance Evaluations. Master’s thesis, University of Waterloo (2023)
Sellberg, L., Jönsson, A.: Using random indexing to improve singular value decomposition for latent semantic analysis. In: 6th International Conference on Language Resources and Evaluation, Marrakech, Morocco, May 26-June 1, 2008. European Language Resources Association (2008)
Shen, D., Zhu, H., Zhu, C., Xu, T., Ma, C., **ong, H.: A joint learning approach to intelligent job interview assessment. In: IJCAI, vol. 18, pp. 3542–3548 (2018)
Shivakumar, P.G., Georgiou, P.G.: Confusion2vec: towards enriching vector space word representations with representational ambiguities. PeerJ Comput. Sci. 5, e195 (2019). https://doi.org/10.7717/peerj-cs.195
Srisayekti, W., Fitriana, E., Moeliono, M.F.: The Indonesian version of the Liebowitz social anxiety scale-self report (LSAS-SR-Indonesia): psychometric evaluation and analysis related to gender and age. Open Psychol. J. 16(1), e221227 (2023)
Valdez, D., Pickett, A.C., Goodson, P.: Topic modeling: latent semantic analysis for the social sciences. Soc. Sci. Q. 99(5), 1665–1679 (2018)
Verma, A., Lamsal, K., Verma, P.: An investigation of skill requirements in artificial intelligence and machine learning job advertisements. Ind. High. Educ. 36(1), 63–73 (2022)
Zeng, Z., Ye, L., Liu, R., Cui, Z., Wu, M., Sha, Y.: Fake news detection by using common latent semantics matching method. In: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1059–1066. IEEE (2021)
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Akkol, E., Olucoglu, M., Dogan, O. (2023). Topic Modeling for Skill Extraction from Job Postings. In: Ortiz-Rodriguez, F., Villazón-Terrazas, B., Tiwari, S., Bobed, C. (eds) Knowledge Graphs and Semantic Web. KGSWC 2023. Lecture Notes in Computer Science, vol 14382. Springer, Cham. https://doi.org/10.1007/978-3-031-47745-4_20
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