Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model. (10th April 2021)

• Record Type: Journal Article
• Authors: Asghar, Junaid; Akbar, Saima; Asghar, Muhammad Zubair; Ahmad, Bashir; Al-Rakhami, Mabrook S.; Gumaei, Abdu
• Other Names: Bangyal Waqas Haider Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2021(2021)
• Publication Date: 2021-04-10
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2021/5512241
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : Nowadays, there is a digital era, where social media sites like Facebook, Google, Twitter, and YouTube are used by the majority of people, generating a lot of textual content. The user-generated textual content discloses important information about people's personalities, identifying a special type of people known as psychopaths. The aim of this work is to classify the input text into psychopath and nonpsychopath traits. Most of the existing work on psychopath's detection has been performed in the psychology domain using traditional approaches, like SRPIII technique with limited dataset size. Therefore, it motivates us to build an advanced computational model for psychopath's detection in the text analytics domain. In this work, we investigate an advanced deep learning technique, namely, attention-based BILSTM for psychopath's detection with an increased dataset size for efficient classification of the input text into psychopath vs. nonpsychopath classes.