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A Novel Text Mining Approach to Sexual Harassment Detection of Case Suspects
Sundar Krishnan, Narasimha Shashidhar, Cihan Varol, ABM Rezbaul Islam
Pages - 17 - 29     |    Revised - 30-06-2022     |    Published - 01-08-2022
Volume - 11   Issue - 1    |    Publication Date - August 2022  Table of Contents
Digital Forensic Analytics, Digital Forensics, Sexual Harassment, Supervised Learning, Hybrid Learning, Unsupervised Learning, Legal Analytics, eDiscovery, Electronic Stored Information, Case Investigation.
Sexual harassment cases often go unreported and can be difficult for an investigator to detect when working with large volumes of digital evidence of an investigation. Artificial Intelligence can be a promising solution to help identify instances of sexual harassment, especially from written communication. In this research, a comprehensive approach to detect indicators of sexual harassment is proposed using supervised and unsupervised learning coupled with the application of Bidirectional Encoder Representations from Transformers (BERT) and Snips NLU. The models are then applied against synthetic digital forensic evidence data for detection of sexual harassment indicators from textual digital evidence.
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Mr. Sundar Krishnan
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Associate Professor Narasimha Shashidhar
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Professor Cihan Varol
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America
Associate Professor ABM Rezbaul Islam
Department of Computer Science, Sam Houston State University, Huntsville, TX - United States of America