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https://github.com/exios66/nlp_sentiment_analysis
https://github.com/exios66/nlp_sentiment_analysis
Last synced: 13 days ago
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- Host: GitHub
- URL: https://github.com/exios66/nlp_sentiment_analysis
- Owner: Exios66
- License: mit
- Created: 2024-12-18T05:49:20.000Z (24 days ago)
- Default Branch: main
- Last Pushed: 2024-12-20T05:25:42.000Z (22 days ago)
- Last Synced: 2024-12-20T06:25:10.044Z (22 days ago)
- Language: Python
- Size: 20.5 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Mental Health Text Sentiment Analysis Pipeline
## Overview
This project implements a comprehensive Natural Language Processing (NLP) pipeline for analyzing sentiment in mental health-related text data. It includes tools for data collection, preprocessing, sentiment analysis, and visualization of mental health discussions.
## Features
- Data Collection: Reddit API integration for mental health subreddits
- Text Preprocessing: Advanced NLP preprocessing using NLTK and spaCy
- Sentiment Analysis: Multiple models including VADER and BERT-based approaches
- Visualization: Interactive dashboards and statistical analysis
- Data Storage: Efficient data management and caching
- API: RESTful API for model serving
## Project Structure
```
├── data/ # Data storage
│ ├── raw/ # Raw collected data
│ ├── processed/ # Preprocessed data
│ └── models/ # Trained models
├── src/ # Source code
│ ├── data_collection/ # Scripts for data collection
│ ├── preprocessing/ # Text preprocessing modules
│ ├── models/ # Model implementation
│ ├── visualization/ # Visualization tools
│ └── api/ # API implementation
├── notebooks/ # Jupyter notebooks for analysis
├── tests/ # Unit tests
├── config/ # Configuration files
└── requirements.txt # Python dependencies
```
## Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/NLP_Sentiment_Analysis.git
cd NLP_Sentiment_Analysis
```
1. Create a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Set up environment variables:
```bash
cp .env.example .env
# Edit .env with your API keys and configurations
```
## Usage
1. Data Collection:
```bash
python src/data_collection/reddit_scraper.py
```
1. Preprocessing:
```bash
python src/preprocessing/preprocess.py
```
3. Model Training:
```bash
python src/models/train.py
```
4. Run Analysis:
```bash
python src/visualization/analyze.py
```
5. Start API:
```bash
python src/api/app.py
```
## Configuration
- Configure data sources in `config/data_sources.yaml`
- Model parameters in `config/model_config.yaml`
- API settings in `config/api_config.yaml`
## Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Authors
- Your Name
## Acknowledgments
- NLTK
- spaCy
- Transformers
- PRAW (Python Reddit API Wrapper)