Incorporating Multi-Stage Machine Learning and Fuzzy Methods for Efficient Cyber-Hate Detection

Abstract

The project's main goal is to address the alarming problem of cyber-hatred, which has greatly increased since social media platforms have become widely used. It recognizes how critical it is to address this issue in the context of the digital world. The initiative suggests using a variety of deep learning and machine learning methods to fight cyber-hate. These consist of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Naive Bayes, and Logistic Regression. When it comes to finding, categorizing, or examining trends in hate speech or objectionable content, each of these techniques probably has a distinct function. Using optimization techniques like Particle Swarm Optimization and Genetic Algorithms, the study applies two classifiers to hate speech data and improves their performance. These optimization methods are probably used to increase the classifiers' accuracy in identifying instances of cyber-hate. Furthermore, the use of Fuzzy Logic seeks to improve the understanding of text data by taking into consideration its intrinsic complexity and subtleties. The main objective is to provide a more practical and efficient method for detecting cyber-hatred. This entails applying a critical thinking viewpoint, which probably entails taking into account contextual clues and minute details that go beyond overt words or phrases. Additionally, the use of fuzzy logic-based systems and optimization approaches aims to develop a more nuanced understanding of hate speech, improving the detection process' accuracy and aligning it with the intricacies of the actual world. By using sophisticated ensemble approaches, namely 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. The cyber-hate detection system's overall efficacy is increased by utilizing these ensemble models.