Application of Deep Learning Methods for Employee Satisfaction Analysis Based on Text Data

Abstract

The application of deep learning methods to analyze employee satisfaction based on text data is investigated. A critical review of existing approaches to assessing employee satisfaction is conducted, and the need to use natural language processing methods and deep neural networks is substantiated. Based on an extensive open dataset of employee reviews, a model is developed that allows for effective classification of texts by satisfaction levels. A thematic analysis of the main causes of positive and negative reviews is carried out using the topic modeling methods Latent Dirichlet Allocation and Non-Negative Matrix Factorization. The results of the study demonstrate the high accuracy of the proposed model and its practical significance for improving HR processes in organizations.