SentimentIQ V2

SentimentIQ V2 is a full-stack web application that analyzes Amazon product reviews and provides actionable insights using a custom-built NLP engine. It also includes a product comparison feature and a UI module for X (Twitter) analysis.

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🚀 Overview

This project helps users answer:

“Should I buy this product or not?”

It processes real customer reviews and generates:

• Sentiment analysis
• Rating distribution
• Aspect-based insights
• Comparison between products

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✨ Features

✅ Amazon Review Analysis

• Paste Amazon product link
• Extract real reviews using Puppeteer
• Analyze sentiment (positive / negative / neutral)
• Generate rating distribution (1★–5★)
• Provide overall score and verdict

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🔄 Product Comparison

• Compare 2–3 Amazon products

• Sequential analysis (safe from blocking)

• Shows:

  • Overall score
  • Sentiment breakdown
  • Rating distribution

• Highlights best product

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🧠 NLP Engine (Frontend)

• Pure JavaScript (no APIs)
• Rule-based sentiment scoring
• Aspect detection (quality, price, delivery, etc.)

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🎨 UI Enhancements

• Animated background (Squares)

• Tab-based navigation:

  • Amazon
  • Compare
  • X (UI ready)

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⚠️ X (Twitter) Analysis

• UI implemented
• Backend scraping not stable yet (in progress)
• Will support hashtag-based sentiment analysis

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🛠️ Tech Stack

Frontend:

• React 18
• Custom CSS / Tailwind
• Framer Motion (optional animations)

Backend:

• Node.js
• Express
• Puppeteer (Amazon scraping)

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▶️ How to Run

1. Start Backend

cd backend
npm install
npm start

Runs on:

http://localhost:5000

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2. Start Frontend

Open a new terminal:

cd frontend
npm install
npm start

Runs on:

http://localhost:3000

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🔍 How It Works

1. User enters Amazon product URL

2. Backend (Puppeteer) scrapes reviews

3. Frontend NLP engine analyzes reviews

4. Dashboard displays:

   • sentiment
   • rating distribution
   • insights
   • recommendation

For comparison:

• Each product is analyzed individually (sequentially)
• Results are aggregated and displayed side-by-side

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⚠️ Limitations

• Amazon may block scraping if too many requests are sent quickly
• Sequential processing is used to avoid detection
• X (Twitter) scraping is not fully implemented yet
• No database (data is processed in-memory)

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🔧 Future Improvements

• Stable X (Twitter) scraping
• Better sentiment accuracy (ML-based model)
• More aspect categories
• Historical tracking (database)
• Multi-platform support (Flipkart, etc.)

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📌 Notes

• No API keys required
• Fully local system
• Designed for demo + educational use

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🚧 Status

Project is functional for:

• Amazon Analysis ✔
• Product Comparison ✔

Work in progress:

• X (Twitter) Analysis 🔄

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👨‍💻 Author

Built as an advanced sentiment analysis and decision-support system.

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⭐ Contribution

Open to improvements, bug fixes, and feature suggestions.