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Link to original content: https://unpaywall.org/10.1007/978-3-031-48316-5_5
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Analysing Online Review by Bank Employees: A Predictive Analytics Approach

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Information Integration and Web Intelligence (iiWAS 2023)

Abstract

Publicly available data on the web is a rich and important resource for generating valuable insights about our world. In this paper, we present the analysis of bank employee online review data obtained from an online platform that collects anonymous employee reviews about the companies they work(ed) with. The feature of anonymity here is important, it helps (especially existing) employees to review their employers freely, without fear of repercussions. Employers can also capitalise on this platform to better understand their employees’ opinions on the company’s performance. However, there are several common issues associated with the data found on such platforms. These include: (i) relatively small number of reviews associated with each employer, (ii) multicollinearity among predictors, and (iii) missing values in the inputs. We propose a solution framework to address these issues. Firstly, we show that a transfer learning approach can help to augment the data size by combining data from different employers. Secondly, we address the missing value issues, which include the use of a rather uncommon method known as missing value replacements using a proxy variable. Finally, we apply a decision tree approach to build reasonably reliable model despite the presence of multicollinearity in the predictors. Our results show that all these solutions put together help to generate a more robust and comprehensible model. In addition, the results also show that augmentation of data is key to address some of the fundamental issues typically encountered in this type of online review data. Finally, we believe that the presented solution can be adapted or extended to other analytics projects that involve the analyses of online review data.

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References

  1. Ajit, P.: Prediction of employee turnover in organizations using machine learning algorithms. Algorithms 4(5), C5 (2016)

    Google Scholar 

  2. Atef, M., Elzanfaly, D.S., Ouf, S.: Early prediction of employee turnover using machine learning algorithms. Int. J. Electr. Comput. Eng. Syst. 13(2), 135–144 (2022)

    Google Scholar 

  3. Breiman, L., Friedman, J., Olshen, R., Stone, C.: CART. Classification and regression trees (1984)

    Google Scholar 

  4. IBM Corp: IBM SPSS Modeler (Version 18.2.2). IBM Corp., Armonk (2018)

    Google Scholar 

  5. Jain, R., Nayyar, A.: Predicting employee attrition using XGBoost machine learning approach. In: 2018 International Conference on System Modeling & Advancement in Research Trends (Smart), pp. 113–120. IEEE, November 2018

    Google Scholar 

  6. Kass, G.V.: An exploratory technique for investigating large quantities of categorical data. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 29(2), 119–127 (1980)

    Google Scholar 

  7. Koncar, P., Helic, D.: Employee satisfaction in online reviews. In: Aref, S., et al. (eds.) SocInfo 2020. LNCS, vol. 12467, pp. 152–167. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60975-7_12

  8. Loh, W.Y., Shih, Y.S.: Split selection methods for classification trees. Stat. Sin., 815–840 (1997)

    Google Scholar 

  9. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  10. Quinlan, J.R.: Bagging, boosting, and C4. 5. In: Aaai/Iaai, vol. 1, pp. 725–730, August 1996

    Google Scholar 

  11. Zhang, H., Xu, L., Cheng, X., Chao, K., Zhao, X.: Analysis and prediction of employee turnover characteristics based on machine learning. In: 2018 18th International Symposium on Communications and Information Technologies (ISCIT), pp. 371–376. IEEE, September 2018

    Google Scholar 

  12. Zhao, Y., Hryniewicki, M.K., Cheng, F., Fu, B., Zhu, X.: Employee turnover prediction with machine learning: a reliable approach. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2018. AISC, vol. 869, pp. 737–758. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01057-7_56

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Correspondence to Priyanka Gupta .

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Emmanuel, D.D.A.A., Tan, S.C., Gupta, P. (2023). Analysing Online Review by Bank Employees: A Predictive Analytics Approach. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_5

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  • DOI: https://doi.org/10.1007/978-3-031-48316-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48315-8

  • Online ISBN: 978-3-031-48316-5

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