Skip to main content
Springer Nature Link
Log in

Using Text Mining and Tokenization Analysis to Identify Job Performance for Human Resource Management at the University of Phayao

  • Conference paper
  • First Online:
Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1863))

Included in the following conference series:

  • 487 Accesses

Abstract

A significant problem in many Thai university organizations is the inability to effectively identify employees’ roles and functions. This research aims to study the workload management of university personnel by text mining techniques. There are two main research objectives. The first objective is to manipulate highly complex Thai word segmentation. The second objective is to produce a predictive model for identifying job performance for human resource management of university personnel. Research tools are machine learning algorithms and word segmentation analysis, including Decision Tree (DT), Generalized Linear Model (GLM), K-Nearest Neighbors (K-NN), Naïve Bayes (NB), Support Vector Machine (SVM), Term Frequency-Inverse Document Frequency (TF-IDF), Term Frequency (TF), Term Occurrences (TO), and Binary Term Occurrences (BTO) techniques. The research data is compiled from job descriptions for three positions from the School of Information and Communication Technology at the University of Phayao. The results show that the best predictive model is developed with the Generalized Linear Model (GLM). It has a high accuracy value of 89.80%, with Binary Term Occurrences (BTO) technique. Research operational plan for future work, researchers plan to develop an information system to support work within the School of Information and Communication Technology, University of Phayao to support further work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Opatha, H.H.D.P.J.: HR analytics: a literature review and new conceptual model. Int. J. Sci. Res. Publ. 10, 130–141 (2020). https://doi.org/10.29322/IJSRP.10.06.2020.p10217

  2. Feng, S.: Job satisfaction, management sentiment, and financial performance: text analysis with job reviews from indeed.com. Int. J. Inf. Manag. Data Insights 3, 100155 (2023). https://doi.org/10.1016/j.jjimei.2023.100155

  3. Gazit, N., Ben-Gal, G., Eliashar, R.: Using job analysis for identifying the desired competencies of 21st-century surgeons for improving trainees selection. J. Surg. Educ. 80, 81–92 (2023). https://doi.org/10.1016/j.jsurg.2022.08.015

    Article  Google Scholar 

  4. Jung, Y., Suh, Y.: Mining the voice of employees: a text mining approach to identifying and analyzing job satisfaction factors from online employee reviews. Decis. Support Syst. 123, 113074 (2019). https://doi.org/10.1016/j.dss.2019.113074

    Article  Google Scholar 

  5. Hoff, K.A., Song, Q.C., Wee, C.J.M., Phan, W.M.J., Rounds, J.: Interest fit and job satisfaction: a systematic review and meta-analysis. J. Vocat. Behav. 123, 103503 (2020). https://doi.org/10.1016/j.jvb.2020.103503

    Article  Google Scholar 

  6. Chen, H., Zhang, Y.: Educating data management professionals: a content analysis of job descriptions. J. Acad. Librariansh. 43, 18–24 (2017). https://doi.org/10.1016/j.acalib.2016.11.002

    Article  MathSciNet  Google Scholar 

  7. Zarindast, A., Sharma, A., Wood, J.: Application of text mining in smart lighting literature - an analysis of existing literature and a research agenda. Int. J. Inf. Manag. Data Insights 1, 100032 (2021). https://doi.org/10.1016/j.jjimei.2021.100032

  8. Albalawi, Y., Buckley, J., Nikolov, N.S.: Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media. J. Big Data. 8, 95 (2021). https://doi.org/10.1186/s40537-021-00488-w

    Article  Google Scholar 

  9. Ousirimaneechai, N., Sinthupinyo, S.: Extraction of trend keywords and stop words from Thai Facebook pages using character n-grams. Int. J. Mach. Learn. 8, 589–594 (2018). https://doi.org/10.18178/ijmlc.2018.8.6.750

  10. Tripathi, G., Naganna, S.: Feature selection and classification approach for sentiment analysis. Mach. Learn. Appl. Int. J. 2, 1–16 (2015). https://doi.org/10.5121/mlaij.2015.2201

    Article  Google Scholar 

  11. Kompan, M., Bieliková, M.: News article classification based on a vector representation including words’ collocations. In: Dicheva, D., Markov, Z., Stefanova, E. (eds.) Third International Conference on Software, Services and Semantic Technologies S3T 2011, pp. 1–8. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23163-6_1

    Chapter  Google Scholar 

  12. Nasa-Ngium, P., Nuankaew, W.S., Nuankaew, P.: Analyzing and tracking student educational program interests on social media with chatbots platform and text analytics. Int. J. Interact. Mob. Technol. 17, 4–21 (2023). https://doi.org/10.3991/ijim.v17i05.31593

    Article  Google Scholar 

  13. Yang, Y., Yu, C., Zhong, R.Y.: Generalized linear model-based data analytic approach for construction equipment management. Adv. Eng. Inform. 55, 101884 (2023). https://doi.org/10.1016/j.aei.2023.101884

    Article  Google Scholar 

  14. Rico-Juan, J.R., Valero-Mas, J.J., Calvo-Zaragoza, J.: Extensions to rank-based prototype selection in k-nearest neighbour classification. Appl. Soft Comput. 85, 105803 (2019). https://doi.org/10.1016/j.asoc.2019.105803

    Article  Google Scholar 

  15. Chen, J., Huang, H., Tian, S., Qu, Y.: Feature selection for text classification with Naïve Bayes. Expert Syst. Appl. 36, 5432–5435 (2009). https://doi.org/10.1016/j.eswa.2008.06.054

    Article  Google Scholar 

  16. Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13, 18–28 (1998). https://doi.org/10.1109/5254.708428

    Article  Google Scholar 

  17. TeCho, J., Nattee, C., Theeramunkong, T.: Boosting-based ensemble learning with penalty profiles for automatic Thai unknown word recognition. Comput. Math. Appl. 63, 1117–1134 (2012). https://doi.org/10.1016/j.camwa.2011.11.062

    Article  Google Scholar 

  18. Haruechaiyasak, C., Kongyoung, S., Dailey, M.: A comparative study on Thai word segmentation approaches. In: 2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp. 125–128 (2008). https://doi.org/10.1109/ECTICON.2008.4600388

Download references

Acknowledgements

This research project was supported by the Thailand Science Research and Innovation Fund and the University of Phayao (Grant No. FF66-UoE002). In addition, this research was supported by many advisors, academics, researchers, students, and staff. The authors would like to thank all of them for their support and collaboration in making this research possible.

Author information

Authors and Affiliations

  1. University of Phayao, Phayao, 56000, Thailand

    Wongpanya S. Nuankaew, Ronnachai Thipmontha, Phaisarn Jeefoo & Pratya Nuankaew

  2. Rajabhat Maha Sarakham University, Maha Sarakham, 44000, Thailand

    Patchara Nasa-ngium

Authors
  1. Wongpanya S. Nuankaew
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ronnachai Thipmontha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Phaisarn Jeefoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patchara Nasa-ngium
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pratya Nuankaew
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pratya Nuankaew .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Siridech Boonsang

  3. Iwate Prefectural University, Iwate, Japan

    Hamido Fujita

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  6. King Mongkut's Institute of Technology, Ladkrabang, Thailand

    Kitsuchart Pasupa

  7. Malaysia Japan International Institute of Technology, Kuala Lumpur, Malaysia

    Ali Selamat

Ethics declarations

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nuankaew, W.S., Thipmontha, R., Jeefoo, P., Nasa-ngium, P., Nuankaew, P. (2023). Using Text Mining and Tokenization Analysis to Identify Job Performance for Human Resource Management at the University of Phayao. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_47

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-42430-4_47

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42429-8

  • Online ISBN: 978-3-031-42430-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation