Open Access

ARTICLE

Personality Assessment Based on Natural Stream of Thoughts Empowered with Machine Learning

Mohammed Salahat¹, Liaqat Ali¹, Taher M. Ghazal^2,3,*, Haitham M. Alzoubi⁴

1 College of Engineering and Technology, University of Science and Technology of Fujairah, Fujairah, UAE
2 School of Information Technology, Skyline University College, University City Sharjah, 1797, UAE
3 Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600, Malaysia
4 School of Business, Skyline University College, University City Sharjah, 1797, Sharjah, UAE

* Corresponding Author: Taher M. Ghazal. Email:

Computers, Materials & Continua 2023, 76(1), 1-17. https://doi.org/10.32604/cmc.2023.036019

Received 14 September 2022; Accepted 03 March 2023; Issue published 08 June 2023

Abstract

Knowing each other is obligatory in a multi-agent collaborative environment. Collaborators may develop the desired know-how of each other in various aspects such as habits, job roles, status, and behaviors. Among different distinguishing characteristics related to a person, personality traits are an effective predictive tool for an individual’s behavioral pattern. It has been observed that when people are asked to share their details through questionnaires, they intentionally or unintentionally become biased. They knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about themselves. Such writings can effectively assess an individual’s personality traits that may yield enormous possibilities for applications such as forensic departments, job interviews, mental health diagnoses, etc. Stream of consciousness, collected by James Pennbaker and Laura King, is one such way of writing, referring to a narrative technique where the emotions and thoughts of the writer are presented in a way that brings the reader to the fluid through the mental states of the narrator. Moreover, computationally, various attempts have been made in an individual’s personality traits assessment through deep learning algorithms; however, the effectiveness and reliability of results vary with varying word embedding techniques. This article proposes an empirical approach to assessing personality by applying convolutional networks to text documents. Bidirectional Encoder Representations from Transformers (BERT) word embedding technique is used for word vector generation to enhance the contextual meanings.

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Keywords

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