Abstract:
Hate Speech phenomenon has taken its roots in social media platforms ever since the dawn of internet and social media to become common interaction place for people from all parts of the world. To prevent racial, gender or various other types of hate on these platforms numerous monitoring ideas and solutions have been proposed over the past for the relevant cyber law authorities. This work presents a Hate Speech Detection Neural Network Model delivered in form of Desktop application. The idea behind the product is to allow a user to detect and monitor the hate speech prevalent on social media platforms by live monitoring of tag-searched tweets and also through a time line of certain suspected user. The tweet is stripped of its original shape through pre-processing and cleaning and fed to the RNN-LSTM model using database as a medium. The model identifies hate speech if found and returns percentage of hate speech in a certain tweet. The detected tweet is then highlighted and displayed in the Desktop application which the user can select and view for more relevant information about user. i
