SentiLog AI is a full-stack application that blends real-time news sentiment analysis with personal mood journaling. It helps users track their emotions and detect news bias using machine learning.
| Layer | Technology |
|---|---|
| Frontend | React, Vite, Tailwind CSS |
| Backend | Node.js, Express, MongoDB |
| ML API | Python, Flask, VaderSentiment |
| Auth | JWT-based Authentication |
| DB | MongoDB Atlas (via Mongoose) |
graph LR
A[React Frontend] --> B[Node.js/Express API]
B --> C[MongoDB]
B --> D[Flask ML API]
D --> E[Sentiment Analysis]
git clone https://github.com/openml-stack/SentiLog-AI.git
cd SentiLog-AIcd client
npm install
npm run devcd ../server
npm install
cp .env.example .env
# Fill your MongoDB URI and other secrets in .env
npm run devcd ../ml-api
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python app.pyUpdate .env in server/ folder with:
PORT=8080
MONGODB_URI=mongodb://localhost:27017/sentilog
ML_API_URL=http://localhost:8060
JWT_SECRET=your_secret_key
You can find detailed API information in server/API_DOC.md, including:
- All Server and ML API endpoints
- Request/response formats
- Sample payloads
- Status codes & error handling
Additionally, the Swagger documentation is available at:
http://localhost:8080/api-docs
- Classifies articles as Left, Right, or Neutral.
- Real-time NLP-based sentiment + ideological analysis.
- Users log their mood and track emotional trends.
- Analyzed using ML-based sentiment/emotion scoring.
SentiLog-AI/
├── client/ # Frontend (React)
├── server/ # Backend (Express + MongoDB)
├── ml-api/ # Python Flask ML service
├── notebook/ # Research notebooks
├── README.md
└── server/API_DOC.md # Detailed API docs
- Comment on an open issue.
- Fork the repository.
- Work in a feature branch.
- Open a pull request with descriptive title.
MIT License — see LICENSE
Let’s build the future of sentiment + media intelligence together.