🧠 Sentiment Analysis of Product Reviews

This project uses VADER Sentiment Analysis from NLTK to classify product reviews into positive, neutral, or negative categories.

📌 Project Overview

We load product reviews, perform sentiment analysis, and visualize the sentiment distribution using Python tools.

📁 Files

• Sentiment_Analysis_With_Extras.ipynb: Main Jupyter Notebook
• data/Reviews.csv: Review data
• requirements.txt: Python dependencies
• .gitignore: Files to exclude from Git

🚀 How to Run

1. Clone this repository:

   git clone https://github.com/yourusername/Sentiment-Analysis-Product-Reviews.git
   cd Sentiment-Analysis-Product-Reviews

2. Install dependencies:

   pip install -r requirements.txt

3. Open the notebook:

   jupyter notebook Sentiment_Analysis_With_Extras.ipynb

4. Make sure data/Reviews.csv is available in the data/ folder.

🛠️ Requirements

• Python 3.7+
• See requirements.txt for full list

📈 Output

• VADER sentiment scores (compound, neg, neu, pos)
• Sentiment labels: positive, neutral, negative
• Visualizations using Seaborn

🙌 Acknowledgements

• NLTK's VADER Sentiment Analyzer