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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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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