Enhanced Hate Speech Detection Using Various Machine Learning Models and Performance Comparison
Vishal Mamluskar, Asad Shaikh, Pranav Mane, Vijay Jumb · 2023
In this hate speech detection system, the team embarked on a mission to combat the rising tide of online hate speech, a prevalent issue in the digital landscape. Leveraging a diverse array of machine learning models, including Convolutional Neural Networks (CNN), Naive Bayes, Linear SVM, Logistic Regression, and Random Forest, the team meticulously evaluated their performance on a dataset sourced from Kaggle. The journey involved extensive data preprocessing, tokenization, and feature engineering, refining the dataset for rigorous analysis. Through a comprehensive assessment of accuracy, precision, recall, and F1-score metrics, the team unveiled the strengths and capabilities of each model. The findings from this provide valuable insights into the effectiveness of these models in identifying instances of hate speech, contributing to the ongoing efforts to foster inclusive and respectful online communication.