Hate Speech Detection Using Supervised Natural Language Processing for Videos and Text

Abstract

Hate speech is an issue that frequently happens when somebody speaks with one another utilizing social media on the Web. Despite the fact that artificial intelligence frameworks are set up to ban such messages. The main issue that arises in these cases is the high false positive rates, so we need mechanisms to help counter such issues and distinguish hate speech without sabotaging the freedom of expression. Likewise, the identification of hate speech in recordings is more difficult than straightforward plain text. In this project we'll research and make a system for detection of hate speech in videos and text utilizing a classification approach in AI with different techniques such as Logistic Regression, SVM, decision tree. The execution of video to text is also something we'll work on. The recent uptick of people using social media has led to a lot of conflicting views, although healthy conversation is an important part of debates some of the content that is being shared online is offensive and can often be downright hateful. It is important to recognize which messages are actually hateful and offensive and which are not to encourage healthy debates over the internet.