feelr - Sentiment Analysis

The Machine Learning model was trained on the Sentiment 140 dataset.

feelr is a web application that packages a machine learning model to analyze text sentiment. It allows users to input text, view sentiment predictions, track their history, and visualize mood trends over time. The app features a secure backend with user authentication and a responsive, user-friendly frontend.

Features

• User Authentication: secure registration, login, and logout using JWT.
• Sentiment Analysis: analyze text and receive sentiment predictions with confidence scores.
• History Tracking: save and view past sentiment analyses in a Twitter-like interface.
• Mood Visualization: view mood trends using interactive graphs.
• Responsive Design: works seamlessly on both desktop only for now.

Tech Stack

• Frontend: React.js, Chart.js
• Backend: Django, Django REST Framework
• Machine Learning: TensorFlow for sentiment prediction
• Database: PostgreSQL

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Setup Instructions:

git clone [email protected]:janymuong/feeler.git

1. Backend

1. Install dependencies:

   cd feeler
   pip install -r requirements.txt

2. Configure .env with the following variables:

   DJANGO_SECRET_KEY=your_secret_key
   DB_NAME=feeler # database
   DB_USER=your_db_user
   DB_PASSWORD=your_db_password
   DB_HOST=localhost
   DB_PORT=5432
   

3. Run migrations and start the server:

   python manage.py migrate
   python manage.py runserver

2. Frontend

1. Navigate to the frontend directory:

   # do in root directory;
   cd FE-feeler

2. Install dependencies:

   npm install

3. Start the development server:

   npm start

Testing

• Frontend:

  npm test

• Backend:

  python manage.py test

Usage

1. Register or log in to the app.
2. Navigate to the Model page to analyze text sentiment.
3. View past analyses in the Emotion History section.
4. Explore mood trends/visualization in the Emotion Graphs section.
5. Log out.

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Contributors

• Jany Muong
• Joram Kireki
• Gatmach Yuol Nyuon
• Josphat Waweru Thumi
• Akech Atem
• Vincent Ohanga