🔤 NLP Playground: Interactive Text Analysis

A comprehensive web application for exploring Natural Language Processing concepts through interactive demos and real-time text analysis.

🎯 Features

📝 Text Input & File Processing

• Direct text input with analysis button
• File upload support (TXT, PDF, DOCX)
• Real-time text preview and statistics

🔤 Tokenization & Lemmatization

• Interactive word tokenization with color-coded highlighting
• Lemmatization visualization with original vs. lemmatized forms
• Token statistics and analysis

🏷️ Named Entity Recognition (NER)

• Entity highlighting with different colors for each type
• Support for PERSON, ORG, GPE, DATE, TIME, MONEY, and more
• Interactive entity distribution charts
• Detailed entity explanations

📚 Part-of-Speech (POS) Tagging

• Complete POS tagging with descriptions
• Color-coded text highlighting by grammatical role
• POS distribution visualization
• Interactive tooltips with explanations

🌳 Dependency Parsing

• Dependency relations analysis
• Visual representation of grammatical dependencies
• Detailed dependency type explanations

😊 Sentiment Analysis

• Real-time sentiment scoring with interactive gauges
• Polarity and subjectivity measurements
• Sentence-by-sentence sentiment breakdown
• Sentiment trend visualization across text

📈 Word Frequency Analysis

• Top word frequency charts
• Word cloud data tables
• Statistical text analysis

🚀 Quick Start

Prerequisites

• Python 3.7 or higher
• pip package manager

Installation

1. Clone the repository

   git clone https://github.com/saifDoesCode/NLP-Playground.git
   cd nlp-playground

2. Install required packages

   pip install streamlit spacy textblob nltk pandas plotly python-docx PyPDF2

3. Download spaCy English model

   python -m spacy download en_core_web_sm

4. Download NLTK data (optional - handled automatically)

   python -c "
   import nltk
   nltk.download('punkt_tab')
   nltk.download('punkt')
   nltk.download('wordnet')
   nltk.download('averaged_perceptron_tagger')
   nltk.download('stopwords')
   nltk.download('omw-1.4')
   "

Running the Application

streamlit run app.py

The application will open in your browser at http://localhost:8501

🛠️ Usage

1. Input Text: Enter text directly or upload a file (TXT, PDF, DOCX)
2. Click Analyze: Press the "🔍 Analyze Text" button to process
3. Explore Results: Scroll through different analysis sections:
   • Text Statistics
   • Tokenization & Lemmatization
   • Named Entity Recognition
   • POS Tagging
   • Dependency Parsing
   • Sentiment Analysis
   • Word Frequency Analysis

📦 Dependencies

streamlit>=1.28.0
spacy>=3.4.0
textblob>=0.17.0
nltk>=3.7
pandas>=1.5.0
plotly>=5.0.0
python-docx>=0.8.11
PyPDF2>=3.0.0

🏗️ Project Structure

nlp-playground/
├── app.py                 # Main Streamlit application
├── requirements.txt       # Python dependencies
├── README.md             # Project documentation
└── assets/               # Screenshots and demo files
    └── demo.png

🔧 Configuration

The application automatically handles:

• NLTK data downloads with fallback mechanisms
• spaCy model loading with error handling
• File processing for multiple formats
• Robust error handling for missing dependencies

🎨 Screenshots

Main Interface

Text Analysis Features

• Tokenization: Color-coded word and sentence breakdown
• NER: Entity highlighting with type classifications
• POS Tagging: Grammatical role visualization
• Sentiment: Real-time emotion analysis with gauges

🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Setup

# Clone your fork
git clone https://github.com/saifDoesCode/NLP-Playground.git

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install development dependencies
pip install black flake8 pytest

📚 Educational Use

This tool is perfect for:

• Students learning NLP concepts
• Educators demonstrating text processing techniques
• Researchers exploring text analysis methods
• Developers prototyping NLP features

🐛 Troubleshooting

Common Issues

spaCy model not found:

python -m spacy download en_core_web_sm

NLTK data missing: The app automatically downloads required NLTK data, but you can manually install:

python -c "import nltk; nltk.download('all')"

File upload errors:

• Ensure files are in supported formats (TXT, PDF, DOCX)
• Check file size limits (Streamlit default: 200MB)

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• spaCy - Industrial-strength NLP library
• NLTK - Natural Language Toolkit
• TextBlob - Simplified text processing
• Streamlit - Web app framework
• Plotly - Interactive visualizations

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⭐ If you find this project helpful, please give it a star!

Built with ❤️ using Python, Streamlit, and modern NLP libraries.