Which algorithms are used in this hate speech detection project?

The system implements and compares two ensemble algorithms: XGBoost and Random Forest. XGBoost achieved superior performance with 95.7% accuracy and 0.951 F1-score.

What is the accuracy of the hate speech detection model?

The XGBoost model achieves 95.7% accuracy with an F1-score of 0.951 and ROC-AUC of 0.968, demonstrating excellent predictive capability for content moderation.

What features are used for hate speech detection?

The system uses TF-IDF text features combined with 23 engineered numeric features including offensive word counts, sentiment indicators, structural features, and linguistic patterns.

Who can benefit from this hate speech detection system?

Social media platforms, content moderators, online community managers, researchers, and policymakers can use this system for automated content moderation, online safety enforcement, and social media monitoring.

What technologies are used to build this project?

The project is built using Python with NLTK for NLP, Scikit-learn, XGBoost, and imbalanced-learn for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Hate Speech and Offensive Language Detection Using Machine Learning

The Hate Speech and Offensive Language Detection Using Machine Learning system is a comprehensive machine learning solution that detects and classifies tweets into Hate Speech, Offensive Language, and Neither categories. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements XGBoost and Random Forest with enhanced feature engineering and leak-free SMOTE, achieving 95.7% accuracy and 0.951 F1-score.

The system utilizes a labeled tweet dataset with approximately 25,000 records and extracts 23 numeric features including sentiment indicators, offensive word counts, and linguistic patterns, combined with TF-IDF vectorization. It provides a user-friendly Flask-based web interface for real-time tweet analysis, batch prediction, and comprehensive reporting, offering practical applications for social media moderation, content filtering, and online safety enforcement.

Python 3.8+ Machine Learning XGBoost Random Forest NLP NLTK TF-IDF Scikit-learn SMOTE Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Class Hate Speech Detection
XGBoost (95.7% Accuracy)
Random Forest (94.1% Accuracy)
23 Engineered Numeric Features
TF-IDF + Linguistic Feature Fusion
Leak-free SMOTE Cross-Validation
Interactive Visualizations
Real-time Tweet Analysis
Feature Importance Analysis
Flask Web Application

Algorithms Used

⚡ XGBoost

Gradient boosting with n_estimators=300, max_depth=8, learning_rate=0.05, L1/L2 regularization

🎯 Accuracy: 95.7%

🌲 Random Forest

Ensemble learning with n_estimators=300, max_depth=15, class_weight='balanced'

🎯 Accuracy: 94.1%

📝 TF-IDF Vectorization

Text feature extraction with n-gram range (1,3) and 5000 features

📊 Top Feature: offensive_count

🔧 Feature Engineering

23 numeric features: sentiment, offensive words, structural indicators

📊 CV F1-Macro: 0.945

Methodology & Workflow

1 Data Collection
~25,000 labeled tweets from social media

2 Data Preprocessing
Cleaning, lemmatization, feature extraction, TF-IDF

3 Feature Engineering
23 numeric features (sentiment, offensive counts, structural)

4 Model Training
XGBoost and Random Forest with leak-free SMOTE

5 Model Evaluation
Accuracy, Precision, Recall, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time tweet analysis

Model Performance Comparison

Metric	Random Forest	XGBoost	Best
Test Accuracy	94.1%	95.7%	XGBoost
Test Precision	93.8%	95.4%	XGBoost
Test Recall	94.0%	95.6%	XGBoost
Test F1-Score	93.6%	95.1%	XGBoost
Test ROC-AUC	95.4%	96.8%	XGBoost
CV Mean F1-Macro	92.8%	94.5%	XGBoost

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Hate Speech Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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