Sentiment Analysis Through Machine Learning for the Support on Decision-Making in Job Interviews

Abstract

In this paper, we propose a sentiment analysis model using machine learning for the support on decision-making in the process of job interviews. To do this, a characterization of the analysis of sentiments, job interviews and machine learning algorithms is first performed. Then, supervised machine learning with artificial neural networks is implemented in a prototype, due to the non-linear behavior described in the variables taken in the study and applying the Eye tracking technique. Finally, tests are carried out with people, in which, by asking questions of these, the involuntary movements of the pupil of the eye are analyzed, through the processing of a volume of data and the results of the ocular patterns are interpreted. Correlated with the questions of the test and with it, a final judgment is presented for the support of the decision making.