Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Big Five Personality Detection Using Deep Convolutional Neural Networks

Version 1 : Received: 12 September 2021 / Approved: 13 September 2021 / Online: 13 September 2021 (09:59:25 CEST)

How to cite: Tinwala, W.; Rauniyar, S. Big Five Personality Detection Using Deep Convolutional Neural Networks. Preprints 2021, 2021090199 (doi: 10.20944/preprints202109.0199.v1). Tinwala, W.; Rauniyar, S. Big Five Personality Detection Using Deep Convolutional Neural Networks. Preprints 2021, 2021090199 (doi: 10.20944/preprints202109.0199.v1).

Abstract

Personality is the most critical feature that tells us about an individual. It is the collection of the individual’s thoughts, opinions, emotions and more. Personality detection is an emerging field in research and Deep Learning models have only recently started being developed. There is a need for a larger dataset that is unbiased as the current dataset that is used is in the form of questionnaires that the individuals themselves answer, hence increasing the chance of unconscious bias. We have used the famous stream-of-consciousness essays collated by James Pennbaker and Laura King. We have used the Big Five Model often known as the five-factor model or OCEAN model. Document-level feature extraction has been performed using Google’s word2vec embeddings and Mairesse features. The processed data has been fed into a deep convolutional network and a binary classifier has been used to classify the presence or absence of the personality trait. Hold- out method has been used to evaluate the model, and the F1 score has been used as the performance metric.

Keywords

Big Five; Natural Language Processing; Personality Detection; Artificial Intelligence

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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