FeedPlus - Feedback Analysis Dashboard

FeedPlus is an enterprise-grade feedback management system that leverages AWS services to analyze customer feedback and provide business intelligence insights to product management teams.

Project Overview

A complete feedback management solution where:

• Customers submit product reviews and feedback
• AWS Comprehend analyzes sentiment and extracts key phrases
• Managers view comprehensive feedback analytics on a professional dashboard
• All data flows directly to AWS S3 and Lambda for real-time processing

Key Features

• Product Catalog - Display products linked to AWS S3
• Customer Registration - Secure user authentication
• Feedback Submission - Simple feedback submission form
• Sentiment Analysis - AWS Comprehend integration (POSITIVE, NEGATIVE, NEUTRAL, MIXED)
• Interactive Dashboard - Real-time feedback analytics
• Customer Profile - View all submitted reviews and feedback history
• Product Performance - Track sentiment trends by product
• Responsive Design - Works on desktop, tablet, and mobile
• Cloud Ready - Production-ready cloud deployment

Technology Stack

Frontend:

• React 18.2 with React Router
• Bootstrap 5 for responsive design
• Chart.js for data visualization
• Axios for API requests

Backend:

• Flask 3.0 (Python web framework)
• Flask-CORS for cross-origin requests
• Python-dotenv for environment configuration
• boto3 for AWS integration

AWS Services:

• S3 for data storage
• Lambda for automated processing
• Amazon Comprehend for sentiment analysis
• Cloud9 for deployment (optional)

Project Structure

FeedPlus/
├── backend/
│   ├── app.py                 # Flask API routes and main application
│   ├── s3_adapter.py          # S3 read/write functions
│   ├── models/                # Data model definitions
│   ├── routes/                # API endpoint routes
│   ├── services/              # Business logic
│   └── utils/                 # Helper functions
├── frontend/
│   ├── src/
│   │   ├── components/        # React components
│   │   ├── pages/             # Page components
│   │   ├── styles/            # CSS stylesheets
│   │   └── utils/             # Utility functions
│   ├── package.json           # Frontend dependencies
│   └── vite.config.js         # Vite build configuration
├── services/
│   └── sentiment_analyzer.py  # Sentiment analysis logic
├── lambda_functions/
│   └── process_feedback_lambda.py # AWS Lambda function
├── data/
│   └── (Local reference files)
├── requirements.txt           # Python dependencies
└── .env.example              # Environment variable template

System Architecture

┌─────────────────┐
│   React App     │ ← Customer & Manager Interface
├─────────────────┤
│  Flask Backend  │ ← REST API / Session Management
├─────────────────┤
│   AWS Services  │
├─────────────────┤
│  S3 Buckets     │ ← Data Storage
│  Lambda         │ ← Automated Processing
│  Comprehend     │ ← Sentiment Analysis
└─────────────────┘

Data Flow

1. User submits feedback → Web form
2. Flask backend → Receives and processes request
3. S3 Storage → Saves to feedbacks.json and incoming/ folder
4. Lambda Trigger → Automatically invoked by S3 event
5. AWS Comprehend → Analyzes sentiment and extracts key phrases
6. Results Storage → Saved to output bucket
7. Dashboard → Displays real-time analytics

AWS Configuration

S3 Buckets:

• your-input-bucket - Stores customers, products, employees, feedback
• your-output-bucket - Stores Comprehend analysis results

S3 Directory Structure:

your-input-bucket/
├── data/
│   ├── customer-dir/
│   │   └── customers.json
│   ├── employee-dir/
│   │   └── employees.json
│   ├── product-dir/
│   │   └── products.json
│   └── feedback-dir/
│       ├── feedbacks.json
│       └── incoming/ (triggers Lambda)

Lambda Function:

• Trigger: S3 ObjectCreated event
• Bucket: your-input-bucket
• Prefix: data/feedback-dir/incoming/
• Action: Analyze feedback with Comprehend, store results to output bucket

Dashboard Features

Feedback Scorecard:

• Total feedback count
• Sentiment distribution (positive, negative, neutral, mixed)
• Percentage breakdown by sentiment
• Negativity spike alerts

Top Key Phrases:

• Most common positive phrases mentioned by customers
• Most common negative phrases mentioned by customers
• Relevance scores for each phrase

Emerging Issues:

• Recent high-impact negative feedback
• Organized by newest first
• Includes product and feedback text

Product Performance:

• Feedback volume by product
• Sentiment distribution per product
• Visual treemap showing relative size

Data Storage and History

All feedback and analysis data is stored in AWS S3 with automatic history preservation:

Feedback Data:

• Location: your-input-bucket/data/feedback-dir/feedbacks.json
• Content: Complete list of all feedback submitted
• Updated: Each time new feedback is submitted
• Retention: Permanent (never automatically deleted)

Analysis Data:

• Location: your-output-bucket/comprehend/{feedback_id}.json
• Content: Sentiment analysis results for each feedback
• Updated: Each time Lambda processes new feedback
• Retention: Permanent for each unique feedback

Historical Data Access:

• Dashboard reads complete feedback list with all history
• No data is lost or purged
• Can view any historical feedback and its analysis
• Dashboard supports filtering by date, product, or sentiment

Getting Started

See SETUP_GUIDE.md for local development setup.

See DEPLOYMENT_GUIDE.md for AWS deployment instructions.

Quick Installation

Prerequisites:

• Python 3.9 or higher
• Node.js 14 or higher
• AWS account with S3 access
• AWS credentials (Access Key and Secret Key)

Setup:

# Clone repository
git clone https://github.com/your-username/FeedPlus.git
cd FeedPlus

# Create Python virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\Activate.ps1

# Install Python dependencies
pip install -r requirements.txt

# Configure AWS credentials
cp .env.example .env
# Edit .env with your AWS credentials

# Install frontend dependencies
cd frontend
npm install
npm run build
cd ..

# Start the backend
python backend/app.py

Access the application at: http://localhost:5000

Testing the Application

Submit Feedback:

1. Register a customer account
2. Browse products
3. Submit feedback for a product
4. Feedback is saved to S3
5. Lambda function processes automatically
6. View results on dashboard

Verify Data Flow:

1. Check S3 bucket for saved feedback
2. Check CloudWatch logs for Lambda execution
3. Check output bucket for analysis results
4. Refresh dashboard to see new insights

Requirements Checklist

• Minimum 5 products on catalog
• Pie charts for sentiment analysis
• Customer registration & authentication
• Customer profile with review history
• AWS Lambda for feedback processing
• AWS Comprehend sentiment analysis
• Real-time dashboard with polling
• S3 JSON storage
• Fully responsive design
• Professional enterprise appearance

License

This project is created for educational purposes.

Support

For issues or questions:

1. Review SETUP_GUIDE.md for setup troubleshooting
2. Review DEPLOYMENT_GUIDE.md for AWS deployment
3. Check CloudWatch logs for Lambda execution
4. Verify AWS S3 bucket structure
5. Check AWS credentials in .env file