Sentiment Analysis for LMS (Face Expressions)

An innovative sentiment analysis system that uses facial expression recognition to provide real-time feedback on student engagement during online learning sessions.

Features

*Real-time Analysis: Live facial expression detection during video sessions

*Sentiment Scoring: Automated sentiment analysis with confidence scores

*Session Reports: Detailed reports on student engagement patterns

*Integration: Seamless integration with existing Learning Management Systems

*Privacy-Focused: Local processing with optional cloud storage

*Multi-platform Support: Works with Zoom, Microsoft Teams, and custom video platforms

Technology Stack

*Frontend: Next.js, React, TypeScript

*Backend: FastAPI (Python)

*AI/ML: OpenCV, TensorFlow for facial recognition

*Database: MongoDB

*Storage: Firebase Storage

*Real-time Communication: WebRTC, Socket.io

Research Foundation

Inspired by IEEE research on affective computing and educational technology, this project explores how computer vision can enhance online learning experiences by providing instructors with insights into student emotional states.

Key Challenges Solved

- Implemented computer vision algorithms for real-time facial expression analysis

- Developed a scalable microservices architecture with Python and Node.js

- Ensured data privacy and ethical AI usage in educational contexts

- Created intuitive visualizations for complex sentiment data

This project represents the intersection of AI, education, and web development, demonstrating my ability to work with cutting-edge technologies to solve real-world problems.