Comments-Toxicity-Detection

Summary

The Comment Toxicity Detection project is designed to identify and classify toxic comments from various sources such as social media platforms, forums, and comment sections. It utilizes machine learning techniques to analyze the textual content of comments and determine whether they contain toxic or harmful language.

Table of Contents

1. Introduction
2. Installation
3. Usage
4. Model Architecture
5. Dataset
6. Performance Evaluation
7. Contributing
8. License

Introduction

In today's digital age, online platforms are often plagued with toxic comments that can negatively impact users' experiences and contribute to online harassment. The Comment Toxicity Detection project addresses this issue by providing a tool for automatically detecting and filtering out toxic comments in real-time.

Installation

To install and set up the Comment Toxicity Detection model, follow these steps:

1. Clone the repository to your local machine:

git clone https://github.com/your_username/comment-toxicity-detection.git

2. Navigate to the project directory:

cd comment-toxicity-detection

3. Install the required dependencies:

pip install -r requirements.txt

Usage

Once installed, you can use the Comment Toxicity Detection model as follows:

python predict_toxicity.py --comment "Your comment text goes here"

Replace "Your comment text goes here" with the actual comment you want to evaluate for toxicity.

Model Architecture

The Comment Toxicity Detection model is built using a deep learning architecture, specifically a recurrent neural network (RNN) or a convolutional neural network (CNN). The model takes textual input data and outputs a toxicity score indicating the likelihood of the comment being toxic.

Dataset

The model is trained on a labeled dataset of comments annotated with toxicity labels. The dataset consists of thousands of comments collected from various online platforms, each labeled as toxic or non-toxic.

Performance Evaluation

The performance of the Comment Toxicity Detection model is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. The model's performance is assessed on both training and validation datasets to ensure robustness and generalization.

Contributing

Contributions to the Comment Toxicity Detection project are welcome! If you'd like to contribute, please follow these guidelines:

1. Fork the repository
2. Create a new branch (git checkout -b feature)
3. Make your changes
4. Commit your changes (git commit -am 'Add new feature')
5. Push to the branch (git push origin feature)
6. Create a new Pull Request

License

This project is licensed under the MIT License. See the LICENSE file for details.

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