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The user-generated textual content discloses important information about people\u2019s 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\u2019s 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\u2019s detection in the text analytics domain. In this work, we investigate an advanced deep learning technique, namely, attention-based BILSTM for psychopath\u2019s detection with an increased dataset size for efficient classification of the input text into psychopath vs. nonpsychopath classes.<\/jats:p>","DOI":"10.1155\/2021\/5512241","type":"journal-article","created":{"date-parts":[[2021,4,10]],"date-time":"2021-04-10T18:51:14Z","timestamp":1618080674000},"page":"1-10","source":"Crossref","is-referenced-by-count":35,"title":["Detection and Classification of Psychopathic Personality Trait from Social Media Text Using Deep Learning Model"],"prefix":"10.1155","volume":"2021","author":[{"given":"Junaid","family":"Asghar","sequence":"first","affiliation":[{"name":"Faculty of Pharmacy, Gomal University, D.I. Khan (KP), Pakistan"}]},{"given":"Saima","family":"Akbar","sequence":"additional","affiliation":[{"name":"Institute of Computing and Information Technology, Gomal University, D.I. 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