Sentiment Analysis Web Application

A simple and elegant web application built with Flask that performs sentiment analysis on user-provided text. The application uses TextBlob's natural language processing capabilities to analyze text and determine its sentiment (positive, negative, or neutral), along with polarity and subjectivity scores.

Features

• Real-time Sentiment Analysis: Analyze the sentiment of any text input instantly
• Polarity Score: Get a numerical polarity score ranging from -1 (negative) to +1 (positive)
• Subjectivity Score: Understand how subjective or objective the text is (0 = objective, 1 = subjective)
• Modern UI: Clean and responsive web interface with smooth animations
• Easy to Use: Simple text input form with clear results display

Technologies Used

• Flask: Python web framework for building the application
• TextBlob: Python library for processing textual data and performing sentiment analysis
• NLTK: Natural Language Toolkit (dependency of TextBlob)
• HTML/CSS: Frontend with modern styling and animations

Requirements

• Python 3.11+ (tested with Python 3.11.2)
• pip (Python package installer)

Installation

1. Clone the repository (or navigate to the project directory):

   cd SentimentAnalysis

2. Create a virtual environment (recommended):

   python3 -m venv venv

3. Activate the virtual environment:

   • On Linux/Mac:

     source venv/bin/activate

   • On Windows:

     venv\Scripts\activate

4. Install dependencies:

   pip install -r requirements.txt

5. Download NLTK data (required for TextBlob):

   python -c "import nltk; nltk.download('punkt'); nltk.download('brown'); nltk.download('movie_reviews')"

Usage

1. Start the Flask application:

   python app.py

2. Open your web browser and navigate to:

   http://localhost:5000
   

3. Enter text in the text area and click "Analyze Sentiment"

4. View the results:

   • Sentiment classification (Positive 😊, Negative 😞, or Neutral 😐)
   • Polarity score (ranges from -1 to +1)
   • Subjectivity score (ranges from 0 to 1)

Project Structure

SentimentAnalysis/
├── app.py                 # Main Flask application
├── templates/
│   └── index.html        # Frontend HTML template
├── venv/                 # Virtual environment (not included in git)
├── requirements.txt      # Python dependencies
└── README.md            # This file

How It Works

1. The user submits text through the web form
2. Flask receives the POST request and extracts the text
3. TextBlob analyzes the text and calculates:
   • Polarity: Measures how positive or negative the text is
   • Subjectivity: Measures how opinionated or factual the text is
4. The sentiment is classified based on polarity:
   • polarity > 0: Positive
   • polarity < 0: Negative
   • polarity = 0: Neutral
5. Results are displayed to the user with color-coded sentiment labels

Example

Input Text:

"I love this product! It's amazing and works perfectly."

Output:

• Sentiment: Positive 😊
• Polarity: 0.625
• Subjectivity: 0.6

Development

The application runs in debug mode by default. To disable debug mode, modify app.py:

if __name__ == "__main__":
    app.run(debug=False)

License

This project is open source and available for educational purposes.

Contributing

Feel free to submit issues, fork the repository, and create pull requests for any improvements..