Automated Offensive Comment Detection Using Text Mining and Deep Learning

Everyone has the right to freedom of expression. However, under the guise of free speech, this privilege is being abused to discriminate against and harm others, either physically or verbally. Hate speech is the term for this type of bigotry. Hate speech is described as language used to show hatred toward an individual or a group of individuals based on traits such as race, religion, ethnicity, gender, nationality, handicap, and sexual orientation. It can take the form of speech, writing, gestures, or displays that target someone due to their affiliation with a particular group. Hate speech has been more prevalent in recent years, both in person and online. Hateful content is bred and shared on social media and other internet platforms, which finally leads to hate crimes. The growing use of social media platforms and information exchange has resulted in significant benefits for humanity. However, this has resulted in several issues, including the spread and dissemination of hate speech messages. Recent studies used a range of machine learning and deep learning techniques with text mining methods to automatically detect hate speech messages on real-time datasets to handle this developing issue on social media platforms. Hence, this paper aims to survey the various algorithms to detect hateful comments and predict the best algorithms in social media datasets. And also implemented in real-time social environments to detect hate speech with mobile intimation.