Text Analysis of Applicants for Personality Classification Using Multinomial Naïve Bayes and Decision Tree

Abstract

Employees' qualities affect companies' performances and with a large number of applicants, it's difficult to find suitable applicants. To help with it, companies carry out psychological tests to know applicants' personalities, since personality's considered to have a relationship with work performances. But psychological testing requires a lot of effort, cost, and human resources. Thus with a system that can classify personalities through text can help reduce the effort needed. Similar studies carried out with the big five personalities as the theoretical basis and used one of the personality traits, namely using the k-NN method with 65% accuracy. Based on these studies, accuracy can improve by finding the best parameters using all of the big five personalities. This research is conducted based on the big five personality traits and related traits, namely consciousness and agreeableness. The data used is text data that's been labelled, pre-processed and feature selected. The clean text data is used to create a classification model using multinomial Naive Bayes and decision trees. There are 6 models built based on 3 work cultures, decision tree with an accuracy of 33%, 66%, 80%, and multinomial naïve Bayes with an accuracy of 83%, 50%, 60%, which resulted as better performance.