International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Analysis of LinkedIn Jobs for Finding High Demand Job Trends Using Text Processing Techniques

  • Kazi, Abdul Karim (Department of Computer Science and Information Technology, NED University) ;
  • Farooq, Muhammad Umer (Department of Computer Science and Information Technology, NED University) ;
  • Fatima, Zainab (Department of Software Engineering, NED University) ;
  • Hina, Saman (Department of Computer Science and Information Technology, NED University) ;
  • Abid, Hasan (Department of Computer Science and Information Technology, NED University)
  • Received : 2022.10.05
  • Published : 2022.10.30
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Abstract

LinkedIn is one of the most job hunting and career-growing applications in the world. There are a lot of opportunities and jobs available on LinkedIn. According to statistics, LinkedIn has 738M+ members. 14M+ open jobs on LinkedIn and 55M+ Companies listed on this mega-connected application. A lot of vacancies are available daily. LinkedIn data has been used for the research work carried out in this paper. This in turn can significantly tackle the challenges faced by LinkedIn and other job posting applications to improve the levels of jobs available in the industry. This research introduces Text Processing in natural language processing on datasets of LinkedIn which aims to find out the jobs that appear most in a month or/and year. Therefore, the large data became renewed into the required or needful source. This study thus uses Multinomial Naïve Bayes and Linear Support Vector Machine learning algorithms for text classification and developed a trained multilingual dataset. The results indicate the most needed job vacancies in any field. This will help students, job seekers, and entrepreneurs with their career decisions

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References

  1. Boselli R, Cesarini M, Mercorio F, Mezzanzanica M. Classifying online job advertisements through machine learning. Future Generation Computer Systems. 2018 Sep 1;86:319-28.. https://doi.org/10.1016/j.future.2018.03.035
  2. Keerthi Kumar HM, Harish BS. Classification of short text using various preprocessing techniques: An empirical evaluation. In Recent findings in intelligent computing techniques 2018 (pp. 19-30). Springer, Singapore.
  3. Gurcan F, Cagiltay NE. Big data software engineering: Analysis of knowledge domains and skill sets using LDA-based topic modeling. IEEE access. 2019 Jun 20;7:82541-52. https://doi.org/10.1109/ACCESS.2019.2924075
  4. Hartmann J, Huppertz J, Schamp C, Heitmann M. Comparing automated text classification methods. International Journal of Research in Marketing. 2019 Mar 1;36(1):20-38. https://doi.org/10.1016/j.ijresmar.2018.09.009
  5. Cvijetic B, Radivojevic Z. Application of machine learning in the process of classification of advertised jobs. International Journal of Electrical Engineering and Computing. 2020 Dec 22;4(2):93-100.
  6. Bansal S, Srivastava A, Arora A. Topic modeling driven content based jobs recommendation engine for recruitment industry. Procedia computer science. 2017 Jan 1;122:865-72. https://doi.org/10.1016/j.procs.2017.11.448
  7. Pejic Bach M, Krstic Z, Seljan S, Turulja L. Text mining for big data analysis in financial sector: A literature review. Sustainability. 2019 Feb 28;11(5):1277. https://doi.org/10.3390/su11051277
  8. Jung Y, Suh Y. Mining the voice of employees: A text mining approach to identifying and analyzing job satisfaction factors from online employee reviews. Decision Support Systems. 2019 Aug 1;123:113074. https://doi.org/10.1016/j.dss.2019.113074
  9. De Mauro A, Greco M, Grimaldi M, Ritala P. Human resources for Big Data professions: A systematic classification of job roles and required skill sets. Information Processing & Management. 2018 Sep 1;54(5):807-17. https://doi.org/10.1016/j.ipm.2017.05.004
  10. Dong T, Triche J. A longitudinal analysis of job skills for entry-level data analysts. Journal of Information Systems Education. 2020 Dec 1;31(4):312.
  11. Amado A, Cortez P, Rita P, Moro S. Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics. 2018 Jan 1;24(1):1-7. https://doi.org/10.1016/j.iedeen.2017.06.002
  12. Bird S, Klein E, Loper E. Natural language processing with Python: analyzing text with the natural language toolkit. " O'Reilly Media, Inc."; 2009 Jun 12.
  13. Ramachandran D, Parvathi R. Analysis of twitter specific preprocessing technique for tweets. Procedia Computer Science. 2019 Jan 1;165:245-51. https://doi.org/10.1016/j.procs.2020.01.083
  14. Hussain A, Ali G, Akhtar F, Khand ZH, Ali A. Design and Analysis of News Category Predictor. Engineering, Technology & Applied Science Research. 2020 Oct 26;10(5):6380-5. https://doi.org/10.48084/etasr.3825
  15. Chopra D, Joshi N, Mathur I. Improving Translation Quality By Using Ensemble Approach. Engineering, Technology & Applied Science Research. 2018 Dec 22;8(6):3512-4. https://doi.org/10.48084/etasr.2269
  16. Khan U, Khan K, Hassan F, Siddiqui A, Afaq M. Towards achieving machine comprehension using deep learning on non-GPU machines. Engineering, Technology & Applied Science Research. 2019 Aug 10;9(4):4423-7. https://doi.org/10.48084/etasr.2734
  17. Kazi AK, Nazir W, Baig MA and Khan S. Breast Cancer Prediction Using Data Mining Classification Techniques. International Journal of Computer Science And Network (IJCSN). 2022 Sep;22(9).