Intelligent Multi-Lingual Cyber-Hate Detection in Online Social Networks: Taxonomy, Approaches, Datasets, and Open Challenges

Abstract

Sentiment Analysis, also known as opinion mining, is the area of Natural Language Processing that aims to extract human perceptions, thoughts, and beliefs from unstructured textual content. It has become a useful, attractive, and challenging research area concerning the emergence and rise of social media and the mass volume of individuals’ reviews, comments, and feedback. One of the major problems, apparent and evident in social media, is the toxic online textual content. People from diverse cultural backgrounds and beliefs access Internet sites, concealing and disguising their identity under a cloud of anonymity. Due to users’ freedom and anonymity, as well as a lack of regulation governed by social media, cyber toxicity and bullying speech are major issues that need an automated system to be detected and prevented. There is diverse research in different languages and approaches in this area, but the lack of a comprehensive study to investigate them from all aspects is tangible. In this manuscript, a comprehensive multi-lingual and systematic review of cyber-hate sentiment analysis is presented. It states the definition, properties, and taxonomy of cyberbullying and how often each type occurs. In addition, it presents the most recent popular cyberbullying benchmark datasets in different languages, showing their number of classes (Binary/Multiple), discussing the applied algorithms, and how they were evaluated. It also provides the challenges, solutions, as well as future directions.