Hate Speech Detection

This project aims to detect hate speech patterns in comments and posts on social media platforms, with a focus on Facebook. The goal is to develop a machine learning model that can classify content as hate speech or not, providing insights into how such speech manifests online.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Model Description
• Results
• Contributing

Overview

The project utilizes a dataset of labeled comments to train a machine learning model capable of identifying hate speech. It involves various steps, including data preprocessing, feature extraction using TF-IDF, and training using Support Vector Machine (SVM) classifiers.

Features

• Data Preprocessing: Clean and preprocess text data to remove noise, stop words, and other irrelevant elements.
• Feature Extraction: Use TF-IDF to convert text data into numerical features suitable for machine learning.
• Model Training: Train an SVM classifier to distinguish between hate speech and non-hate speech.
• Evaluation: Assess model performance using metrics such as accuracy, F1-score, and confusion matrix.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/hate-speech-detection.git

2. Navigate to the project directory:

   cd hate-speech-detection

3. Install the required dependencies:

   pip install pandas numpy matplotlib seaborn scikit-learn nltk

Usage

1. Ensure that you have the required dataset (labeled_data.csv) in the project directory.
2. Open the notebook HATE_SPEECH_DETECTION.ipynb in Jupyter Notebook or JupyterLab.
3. Run the cells sequentially to execute the code, train the model, and evaluate its performance.

Model Description

• Algorithm: Support Vector Machine (SVM)
• Feature Extraction: TF-IDF Vectorization
• Libraries Used:
  • Scikit-learn for model training and evaluation
  • NLTK for text preprocessing
  • Pandas and NumPy for data manipulation
  • Matplotlib and Seaborn for visualization

Results

The performance of the model is evaluated using:

• Accuracy Score: Measures the overall correctness of the model.
• F1 Score: Balances precision and recall, particularly important for imbalanced datasets.
• Confusion Matrix: Provides detailed insight into the model’s predictions.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any bugs, improvements, or suggestions.