📄 TDS-AI-Text-Analyzer

AI-powered text analysis tool by TheDarkSyntax Performs sentiment detection, keyword extraction, language identification, and text summarization using open-source NLP models. Built with Python and Hugging Face Transformers.

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📌 Features

• Sentiment Analysis → Detect whether text is Positive, Negative, or Neutral.
• Keyword Extraction → Identify the most important words/phrases in a document.
• Language Detection → Automatically detect the language of the text.
• Text Summarization → Condense long articles into concise summaries.
• Open Source & Customizable → You can fine-tune or expand the capabilities for your own needs.

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🚀 Tech Stack

• Python 3.9+
• Hugging Face Transformers → Pre-trained NLP models
• NLTK / spaCy → Tokenization & linguistic processing
• Scikit-learn → Machine learning utilities
• FastAPI (Optional) → Turn into a web API
• Pytest → Unit testing

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📂 Project Structure

TDS-AI-Text-Analyzer/
│
├── README.md               # Project documentation
├── requirements.txt        # Dependencies
├── app.py                   # Entry point for running the tool
├── analyzer/               # Core analysis modules
│   ├── __init__.py
│   ├── sentiment.py        # Sentiment analysis logic
│   ├── keywords.py         # Keyword extraction
│   ├── language_detect.py  # Language detection
│   └── summarizer.py       # Text summarization
└── tests/                  # Unit tests
    ├── test_sentiment.py
    ├── test_keywords.py
    ├── test_language.py
    └── test_summarizer.py

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🛠 Installation

1. Clone this repository

git clone https://github.com/YourUsername/TDS-AI-Text-Analyzer.git
cd TDS-AI-Text-Analyzer

2. Create a virtual environment (recommended)

python -m venv venv
source venv/bin/activate   # For Linux/Mac
venv\Scripts\activate      # For Windows

3. Install dependencies

pip install -r requirements.txt

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📖 Usage

1. Run sentiment analysis

from analyzer.sentiment import analyze_sentiment

text = "I love using AI for my projects!"
result = analyze_sentiment(text)
print(result)  # Output: Positive

2. Extract keywords

from analyzer.keywords import extract_keywords

text = "Artificial Intelligence is transforming industries across the globe."
keywords = extract_keywords(text)
print(keywords)

3. Detect language

from analyzer.language_detect import detect_language

text = "Bonjour tout le monde"
lang = detect_language(text)
print(lang)  # Output: French

4. Summarize text

from analyzer.summarizer import summarize_text

long_text = "..."  # Your long paragraph/article
summary = summarize_text(long_text)
print(summary)

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🔍 Example Output

Input:

The movie was absolutely wonderful! The acting was superb and the visuals were stunning.

Output:

Sentiment: Positive
Keywords: ['movie', 'acting', 'visuals']
Language: English
Summary: "The movie was wonderful with superb acting and stunning visuals."

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📅 Roadmap

• Add web interface with Streamlit
• Deploy as FastAPI service
• Add named entity recognition (NER)
• Support more languages for summarization
• Implement real-time text analysis API

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🤝 Contributing

Contributions are welcome!

1. Fork the repository
2. Create a new branch (feature/your-feature)
3. Commit your changes
4. Push to your fork & submit a Pull Request

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📜 License

📜 License

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

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🌟 Credits

Built with ❤️ by TheDarkSyntax using open-source AI models and NLP libraries.