Sentiment Feeder - Event Feedback Analysis Platform

Problem Statement

In today's fast-paced event management landscape, gathering and analyzing feedback effectively is crucial for continuous improvement. Traditional feedback collection methods often face several challenges:

1. Delayed Analysis: Manual processing of feedback forms leads to delayed insights
2. Limited Sentiment Understanding: Basic feedback forms fail to capture the emotional context
3. Data Silos: Feedback data remains scattered and difficult to aggregate
4. Real-time Monitoring: Lack of immediate visibility into event success metrics
5. Resource Intensive: Manual analysis requires significant time and human resources

Our Solution

Sentiment Feeder is a comprehensive event feedback analysis platform that leverages AI to provide real-time sentiment analysis and actionable insights. The platform helps event organizers:

• Collect and analyze feedback in real-time
• Understand participant sentiment through AI-powered analysis
• Track event success metrics through intuitive dashboards
• Make data-driven decisions for future events

Key Features

1. Real-time Sentiment Analysis

• AI-powered sentiment analysis of feedback comments
• Categorization into Positive, Neutral, and Negative sentiments
• Instant visualization of sentiment distribution

2. Interactive Dashboard

• Real-time feedback monitoring
• Sentiment distribution charts
• Event-wise feedback analysis
• Customizable filters and views

3. Event Management

• Create and manage multiple events
• Track feedback across different event types
• Export feedback data for detailed analysis

4. User-friendly Interface

• Clean, modern UI with glassmorphism design
• Responsive layout for all devices
• Intuitive navigation and data visualization

Technical Implementation

Frontend

• React.js for dynamic user interface
• Tailwind CSS for modern, responsive design
• Chart.js for data visualization
• Axios for API integration

Backend

• FastAPI for high-performance API
• MongoDB for flexible data storage
• TextBlob for sentiment analysis
• Rate limiting and security features

Security Features

• API key authentication
• CORS protection
• Rate limiting
• Input validation

Getting Started

Prerequisites

• Node.js (v14 or higher)
• Python 3.8+
• MongoDB

Installation

1. Clone the repository

git clone https://github.com/yourusername/Sentiment_Analysis_WebApp.git

2. Set up the backend

cd backend
pip install -r requirements.txt

3. Set up the frontend

cd frontend
npm install

4. Configure environment variables

• Create .env files in both frontend and backend directories
• Add necessary API keys and configuration

5. Run the application

# Backend
cd backend
uvicorn app.main:app --reload

# Frontend
cd frontend
npm start

Future Enhancements

1. Advanced analytics with machine learning
2. Custom feedback form builder
3. Integration with popular event platforms
4. Automated report generation
5. Multi-language support

Team

• Rahul Rai - Full Stack Developer
• Rahul Prajapati - Backend Developer
• Priyanshu Jaiswal - Frontend Developer

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• TextBlob for sentiment analysis
• FastAPI for the backend framework
• React and Tailwind CSS for the frontend
• Chart.js for data visualization

Snippets