📊 Text Analysis Application

A Python-based text processing program with dual implementation modes (Functional & OOP)

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📋 Project Description

This Text Analysis Application is a Python-based tool that analyzes blocks of text and produces useful statistics including word counts, character counts, sentence detection, and word frequency analysis with visualization.

Key Highlights

• ✅ Two Implementation Versions in a single file
• ✅ Modern GUI with drag-and-drop support
• ✅ Data Visualization using matplotlib
• ✅ Multi-format file support (TXT, PDF, DOCX, RTF)

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🎯 Features

Input Options

Option	Description
Type/Paste	Enter text directly into the text area
Browse File	Load text from .txt, .pdf, .docx, .rtf files
Drag & Drop	Drop files directly onto the application window

Text Processing

• Character Count (with and without spaces)
• Word Count (punctuation ignored)
• Sentence Count (based on ., !, ?)
• Paragraph Count (separated by empty lines)
• Unique Word Frequency Analysis

Visualization

• Bar chart showing:
  • Total Words
  • Total Sentences
  • Total Paragraphs
  • Total Unique Words
• Top 10 Most Common Words display

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🔧 Implementation

Version 1: Functional/Modular

# Standalone functions for each operation
count_characters_with_spaces(text_lines)
count_characters_without_spaces(text_lines)
count_words(text_lines)
count_sentences(text_lines)
count_paragraphs(text_lines)
analyze_word_frequency(text_lines)
get_text_statistics_functional(text_lines)  # Main function

Version 2: Object-Oriented (TextAnalyzer Class)

class TextAnalyzer:
    def __init__(self, text_lines):
        self.text_lines = text_lines
        self.stats = {}
        self.word_freq = {}

    def count_characters_with_spaces(self)
    def count_characters_without_spaces(self)
    def count_words(self)
    def count_sentences(self)
    def count_paragraphs(self)
    def analyze_word_frequency(self)
    def calculate_statistics(self)  # Main method

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📦 Installation

Requirements

• Python 3.x
• tkinter (usually included with Python)

Install Dependencies

pip install matplotlib

Optional Dependencies (for additional file formats)

pip install python-docx    # For .docx files
pip install PyPDF2         # For .pdf files
pip install striprtf       # For .rtf files
pip install tkinterdnd2    # For drag-and-drop support

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🚀 Usage

Run the Application

python text_analyzer.py

Steps

1. Enter text by typing, pasting, or loading a file
2. Select analysis method: Functional or OOP
3. Click "Analyze Text" button
4. View results in the results area and visualization window

Output Format

Total characters (with spaces): x
Total characters (without spaces): y
Total words: z
Total sentences: k
Total paragraphs: s

Top 10 Most Common Words:
Word : Frequency

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📁 Project Structure

python-text-analyzer/
├── text_analyzer.py            # Main application (all code in one file)
├── README.md                   # Project documentation
├── requirements.txt            # Python dependencies
├── sample.txt                  # Sample text for testing
├── LICENSE                     # MIT License
└── .gitignore                  # Git ignore rules

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🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

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

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👨‍💻 Author

AarontheGalaxy

🔗 GitHub Profile: https://github.com/AarontheGalaxy

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📄 License

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

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🐢