NLP Text Processor

A powerful web application for natural language text analysis and preprocessing.

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Features

The NLP Text Processor offers a comprehensive suite of tools for analyzing and transforming text data:

Tokenization

• Word Tokenization: Split text into individual words using NLTK, spaCy, TextBlob, or simple whitespace splitting.
• Sentence Tokenization: Break down text into sentences for granular analysis.

Text Normalization

Clean and standardize your text with multiple operations:

• Lowercasing: Convert all text to lowercase.
• Contraction Correction: Expand contractions (e.g., "don't" -> "do not").
• Punctuation Removal: Strip all punctuation marks.
• Whitespace Cleanup: Remove multiple spaces and trim text.
• Spelling Correction: Automatically correct spelling errors.
• Emoji Conversion: Convert emojis to their text description or remove them.

Advanced Analysis

• Stop Words Removal: Filter out common non-informative words.
• POS Tagging: Identify parts of speech (Nouns, Verbs, Adjectives, etc.).
• Stemming: Reduce words to their root form (e.g., "running" -> "run").
• Lemmatization: Convert words to their base dictionary form (e.g., "better" -> "good").

📈 Visualizations & Sentiment

• Sentiment Analysis: Detect if text is Positive, Negative, or Neutral with polarity and subjectivity metrics.
• Word Cloud: Visualize the most frequent words in your text.

Real-time Statistics

• Instant text metrics including sentence count, token count, and average tokens per sentence.
• Interactive progress tracking during processing.

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Tech Stack

• Frontend: Streamlit
• NLP Libraries:
  • NLTK (Natural Language Toolkit)
  • spaCy
  • TextBlob
• Utilities: emoji, contractions, demoji

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Project Structure

nlp-text-processor/
├── app.py              # Core NLP logic and helper functions
├── gui.py              # Main Streamlit interface application
├── requirements.txt    # Project dependencies
└── README.md           # Project documentation

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Installation & Setup

1. Clone the repository

   git clone https://github.com/utachicodes/nlp_text_processor.git
   cd nlp-text-processor

2. Create a virtual environment (Recommended)

   # Windows
   python -m venv venv
   venv\Scripts\activate
   
   # macOS/Linux
   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Download required NLP data The app will attempt to download NLTK data automatically, but you may need to install the spaCy model manually if it fails:

   python -m spacy download en_core_web_sm

5. Run the application

   streamlit run gui.py

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Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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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License

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