Evaluation of the Emotional State of the employees of the company FenixCorp-ADS using Machine Learning

Abstract

Artificial Intelligence (AI) is currently booming, not only in industrial processes, but also in fields related to activities that contribute to the development of organizations and human knowledge. Machine Learning is contained within the computational context of AI, divided into three main approaches: Supervised Machine Learning (AAS), Unsupervised Machine Learning and Reinforcement Learning, which are techniques and algorithms capable of "learning and reasoning" by simulating the human brain, and allows classifying and predicting the behavior of the data supplied to the chosen model, through the use of the SciKit-Learn library and other Python tools, a programming language widely used in data analysis and sentiment analysis, another tool for extracting information based on subjective opinions. Emotional State (ES) is a conscious and unconscious reaction in response to a specific stimulus at a specific time or situation that individuals have. In order to establish a possible relationship with the job performance of employees and the talent drain of the company FenixCorp-ADS, a questionnaire was developed via Web and the classification and prediction algorithms of supervised and unsupervised machine learning were chosen, which allowed a comparative analysis to determine the most efficient model of sentiment analysis performed, contributing to its application in a more continuous way by business organizations in order to use it to automate these processes.