Topic Modelling and Artificial Intelligence based Method Using Online Employee Assessments to Analyse Job Satisfaction

Year 2022, , 46 - 52, 31.12.2022

Ali Özdemir , Aytuğ Onan , Vildan Çınarlı Ergene

https://doi.org/10.34110/forecasting.1173063

Abstract

In this study, the performance of the proposed sample selection method was evaluated on some basic classifiers by conducting a basic literature review on the use of topic modelling methods by considering the online evaluations of the employees in order to determine and analyse the job satisfaction factors. In addition, the effectiveness of different representation structures is evaluated in order to represent the data sets effectively and the main results are obtained regarding the use of classification ensemble methods in the field of text mining. In this work it was emphasized that machine learning methods can achieve high performance in classification and work effectively and scalable with large data sets. The dataset used in this study was obtained from www.kaggle.com. A total of 67529 comments collected from people working at Google, Amazon, Netflix, Facebook, Apple, and Microsoft were evaluated. Within the scope of this study, a text mining and artificial intelligence-based method will be developed, and a solution will be brought to text mining with artificial intelligence methods.

Keywords

Topic modelling, Text classification, Ensemble learning, Artificial intelligence

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