Unveiling Human Essence: Deep Learning in Personality Traits Detection

Abstract

Natural Language Processing (NLP) and Deep Learning (DL) are two branches of Artiffcial Intelligence (AI). They offer several methods, models and algorithms to facilitate the user work. The ffrst one satisffes the requirement for natural language interaction between a user and the machine. The second one is based on multi-layered neural networks to simulate the human brain function in order to recognize complex patterns in structured and unstructured data. In this work, we propose exploiting the performances of NLP and DL to automate the detection of the personality traits from text. The personality is considered as the combination of various factors including behavior, emotion, thoughts, etc. The detection of personality traits has a crucial impact on our daily life improving, hence, our personal growth, social interactions, professional environments, customer experiences, and more. Many works in the literature proposed solutions to study the different personality traits from text. Because of the huge amount of data extracted from emails, social medias, etc., automating this task becomes crucial hence the use of DL and NLP. To achieve our goal, we implement three different approaches. We evaluate them and we compare them to extract the best one to our case. We used, for this purpose, Big Five dataset which represents the ffve traits of a person which are Openness, Conscientiousness, Extroversion, Agreeableness and Neuroticism. We used, also, NRC Emotion Lexicon which is a list of English words used to facilitate the extraction of emotional nuances from text data.