📊 Amazon Review Sentiment Analysis

✅ Overview

This project analyzes Amazon product reviews using Python, stores data in MySQL, and visualizes insights in Power BI. Cloud integration is demonstrated using Azurite (Azure Blob Storage emulator).

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🚀 Tech Stack

![Python] ![MySQL] ![Power%20BI] ![Azure]

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🏗️ Architecture Workflow

Python (Data Generation + Sentiment Analysis) → MySQL (Relational Storage)
        → Power BI (Visualization) → Azure Blob (Simulated with Azurite)

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📂 Dataset

• Size: 500 Amazon product reviews (synthetic but realistic)
• Features:
  • Customer ID, Product ID, Review Text, Rating
  • Sentiment (Positive/Negative/Neutral)
  • Polarity Score (-1 to +1)

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🛠️ Key SQL Queries

-- Total Reviews
SELECT COUNT(*) FROM amazon_reviews_sentiment;

-- Avg Polarity by Category
SELECT category, ROUND(AVG(polarity), 2) FROM amazon_reviews_sentiment GROUP BY category;

-- Top 10 Products by Positive Reviews
SELECT product_id, COUNT(sentiment) as Positive_Count
FROM amazon_reviews_sentiment
WHERE sentiment='Positive'
GROUP BY product_id
ORDER BY Positive_Count DESC
LIMIT 10;

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📊 Power BI Dashboard

Main Dashboard:

![Main Dashboard]

Drill-through Page:

![Drillthrough Page]

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▶ How to Run

1. Clone this repository:

   git clone <your-repo-url>

2. Install Python dependencies:

   pip install -r requirements.txt

3. Import dataset into MySQL using the provided SQL scripts.

4. Open Power BI and connect to MySQL database for visualization.

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🔮 Future Enhancements

• ✅ Integrate live Azure Blob Storage
• ✅ Use Azure Cognitive Services for advanced NLP
• ✅ Deploy real-time dashboard on Power BI Service

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🔗 Connect with Me

• GitHub
• LinkedIn