Optimized Cyber-Hate Detection With Machine Learning Classifier with Fuzzy Logic

Abstract

The project's main focus is on tackling the alarming problem of cyber-hatred, which has greatly increased as social media platforms have become more widely used. It recognises how urgent and significant it is to address this issue in the context of the digital world. The initiative suggests using a variety of machine learning and deep learning strategies to counteract cyber-hatred. These consist of recurrent neural networks (RNNs), convolutional neural networks (CNNs), logistic regression, and Naive Bayes. Each of these techniques probably has a distinct function in recognising, categorising, or examining trends in hate speech or objectionable material. Using hate speech data, the study applies two classifiers and optimises their performance through the use of genetic algorithms and particle swarm optimisation. These optimisation methods are probably used to increase the classifiers' accuracy in identifying instances of cyberhatred. Furthermore, by taking into consideration the intricacies and subtleties of text material, fuzzy logic is intended to improve understanding. The main objective is to provide a more practical and efficient method for detecting cyberhate. This entails applying a critical thinking viewpoint, which probably entails taking into account subtleties and contextual clues in addition to specific words or phrases. Additionally, the use of fuzzy logic-based systems and optimisation approaches aims to develop a more nuanced understanding of hate speech, improving detection accuracy and bringing it into line with the complexity of the actual world. By using sophisticated ensemble techniques—more especially, a Voting Classifier and a Stacking Classifier—the project expands its potential. The Stacking Classifier's remarkable 100% accuracy shows how reliable it is in spotting instances of cyber hatred. Using these ensemble models improves the cyber-hate detection system's overall efficacy.