Investigating Big Five Personality Factor Patterns through Machine Learning Techniques
||25 July 2019
||2:00 PM - 3:30 PM
||Toowoomba - T357, or via Zoom
||For more information, please contact the Graduate Research School.
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The present study proposes to utilize machine learning techniques, coupled with personality theory, to build upon our theoretical and applied understanding of personality. Specifically, at the individual level personality trait expression forms density distributions, with each of the five factors of personality forming varying bell type distribution curves (along the axis of level of state expression and frequency of state expression). Through the application of machine learning, we will use supervised learning to build a prediction model which taking four of the five trait scores, predict the fifth trait. This imitation of an individual’s trait density distributions, will then be tested again an authorship attribution task, to assess real world performance. In doing the above, the research aims to demonstrate that: density distributions are a site of difference between individuals; density distributions have real world application, and contribute to increasing our understanding of individual personality movement in response to stress.