https://github.com/shafaq-aslam/intelligent-hate-speech-detection-using-bidirectional-lstm
This project utilizes deep learning and NLP techniques to detect hate speech in text, promoting safer online communication and enhancing content moderation.
https://github.com/shafaq-aslam/intelligent-hate-speech-detection-using-bidirectional-lstm
ai-ethics bidirectional-lstm deep-learning hate-speech-detection machine-learning natural-language-processing nlp python text-classification
Last synced: about 1 month ago
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This project utilizes deep learning and NLP techniques to detect hate speech in text, promoting safer online communication and enhancing content moderation.
- Host: GitHub
- URL: https://github.com/shafaq-aslam/intelligent-hate-speech-detection-using-bidirectional-lstm
- Owner: shafaq-aslam
- Created: 2024-10-19T07:47:56.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-11-17T10:33:21.000Z (6 months ago)
- Last Synced: 2025-02-13T21:51:38.018Z (3 months ago)
- Topics: ai-ethics, bidirectional-lstm, deep-learning, hate-speech-detection, machine-learning, natural-language-processing, nlp, python, text-classification
- Language: Jupyter Notebook
- Homepage:
- Size: 1.23 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Intelligent Hate Speech Detection Using Bidirectional LSTM
## Project Overview
This project employs natural language processing (NLP) and deep learning techniques to detect hate speech in text data, contributing to safer online environments.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Dataset](#dataset)
- [Model Architecture](#model-architecture)
- [Results](#results)
- [Contributing](#contributing)
## Installation
To set up the project, clone the repository and install the required packages using the following commands:
```bash
git clone https://github.com/shafaq-aslam/Intelligent-Hate-Speech-Detection-Using-Bidirectional-LSTM.git
cd Hate-Speech-Detection-Using-Deep-Learning
pip install -r requirements.txt
```
## Usage
jupyter notebook Notebook
## Dataset
The dataset used for this project consists of labeled text data containing instances of hate speech and non-hate speech, sourced from various online platforms. Dataset
## Model Architecture
The model architecture typically includes:
- Embedding Layer: Converts words into dense vector representations.
- LSTM/GRU Layers: Captures long-term dependencies in text data.
- Dense Layer: Outputs probabilities for each class (hate speech or not).
## Results
The model demonstrates high accuracy in detecting hate speech, providing valuable insights for moderating online content effectively.
## Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or improvements.