Overview
The project aims to address the increasing prevalence of hate speech on social media by identifying the most accurate machine learning algorithm for detection. We have evaluated five popular machine learning models: Logistic Regression, SVM, Naive Bayes, Random Forest, and KNN.
Introduction
The surging prevalence of hate speech on social media underscores the need for prompt action. Our project focuses on identifying an effective machine learning algorithm to address the impact of hate speech. We have implemented and compared five different algorithms to determine the most accurate model for hate speech detection.
Models
The following machine learning models were utilized in this project:
- Logistic Regression
- Support Vector Machine (SVM)
- Naive Bayes
- Random Forest
- K-Nearest Neighbors (KNN)
Results
After rigorous evaluation, Logistic Regression emerged as the most effective model, boasting an accuracy of approximately 90%.