💬 Sentiment Analyzer

A Python desktop tool that analyzes the sentiment of text — whether it's Positive, Negative, or Neutral — using Natural Language Processing (NLP).

Built for businesses and individuals who want to understand how people feel about their products, services, or content.

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🖼️ What It Does

• Single Text Analysis — Type or paste any text and get instant sentiment feedback
• Bulk CSV / Excel Analysis — Upload a file with hundreds of reviews and analyze all rows at once
• Visual Report — Generates a pie chart showing the sentiment distribution
• Output File — Saves results as a clean CSV with Sentiment + Polarity Score columns
• Simple GUI — No coding needed, just click and analyze

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📸 Sample Output

review	Sentiment	Polarity Score
This product is amazing!	Positive	0.75
Worst purchase ever	Negative	-1.0
The delivery was okay	Neutral	0.0
I love it so much	Positive	0.35
Terrible quality never buying again	Negative	-1.0

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🛠️ Tech Stack

Tool	Purpose
Python 3.12	Core language
TextBlob	NLP sentiment analysis
NLTK	Language processing backend
Pandas	CSV/Excel reading and processing
Matplotlib	Pie chart generation
Tkinter	Desktop GUI
OpenPyXL	Excel file support

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🚀 How to Run

1. Clone the repo

git clone https://github.com/Anuj230977/sentiment-analyzer.git
cd sentiment-analyzer

2. Install dependencies

pip install textblob nltk pandas matplotlib openpyxl

3. Download NLTK data (one-time only)

python -c "import nltk; nltk.download('punkt'); nltk.download('stopwords'); nltk.download('punkt_tab')"

4. Run the app

python analyzer.py

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📂 Project Structure

sentiment-analyzer/
├── analyzer.py          # Main application file
├── README.md            # Project documentation
├── CODE_EXPLANATION.md  # Detailed code walkthrough
└── .gitignore           # Ignores output folders and temp files

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💡 Use Cases

• E-commerce businesses — Analyze customer reviews in bulk
• Restaurants / Hotels — Understand feedback from review exports
• HR Teams — Analyze employee survey responses
• Students — NLP project for data analytics coursework
• Content Creators — Analyze comment sections from YouTube/Instagram

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📊 How Sentiment Scoring Works

TextBlob assigns a polarity score between -1.0 and +1.0 to every piece of text:

Score Range	Sentiment
> 0.2	😊 Positive
-0.2 to 0.2	😐 Neutral
< -0.2	😠 Negative

Common neutral words like "okay", "fine", "alright" in short phrases are automatically classified as Neutral regardless of score.

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👤 Author

Anuj Jadhav

• 🎓 TY BBA-CA Student
• 📧 anuj1230567@gmail.com
• 💼 LinkedIn
• 🐙 GitHub

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📄 License

This project is open source and available under the MIT License.