SpeechSentinel

A web application that detects and classifies hate speech using machine learning. This project preprocesses user input, classifies it into categories such as hate speech, offensive speech, or non-offensive speech, and returns the result.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Technologies
• Model
• Future Enhancements
• Contributing
• License

Overview

SpeechSentinel uses natural language processing (NLP) and a machine learning model to classify text as hate speech, offensive speech, or non-offensive speech. The app preprocesses text input, applies a trained DecisionTreeClassifier, and provides a prediction.

Features

• Input text and analyze for hate speech categories.
• NLP preprocessing of text (removal of stopwords, stemming, etc.).
• Classification into three categories: Hate speech, Offensive speech, or Non-offensive speech.
• Flask-based web interface with input and results pages.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/SpeechSentinel.git

2. Navigate to the project directory:

   cd SpeechSentinel

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the Flask app:

   python app.py

Usage

• Visit http://127.0.0.1:5000/ in your browser.
• Input text in the provided field.
• The app will classify the text as one of three categories:
  • Hate speech
  • Offensive speech
  • No hate and offensive speech

Technologies

• Python 3.12
• Flask
• Pandas
• NumPy
• NLTK for text preprocessing
• scikit-learn for machine learning model (DecisionTreeClassifier)

Model

The model used is a DecisionTreeClassifier trained on a dataset labeled for hate speech and offensive speech. The input text is preprocessed by:

• Converting to lowercase
• Removing URLs and special characters
• Tokenizing and removing stopwords
• Applying stemming

The model achieves basic classification based on word frequency features.

Future Enhancements

• Improve the model by experimenting with more complex algorithms like Random Forest or SVM.
• Add more sophisticated NLP techniques like lemmatization and part-of-speech tagging.
• Expand the dataset for better generalization.
• Implement API endpoints for model prediction.
• Improve the user interface.

Contributing

Feel free to contribute to this project by submitting pull requests or suggesting features. Please open an issue for discussions before major changes.

License

This project is licensed under the MIT License.