README: Noam Chomsky & Michel Foucault - Turkish Book Analysis

📌 Project Overview

This Jupyter Notebook contains an analysis of the Turkish edition of the book featuring Noam Chomsky and Michel Foucault. The book explores their philosophical and linguistic debates, particularly their discussions on human nature, knowledge, power structures, and language.

🎯 Objectives

• Provide an in-depth analysis of key themes and arguments.
• Compare and contrast Chomsky’s and Foucault’s perspectives.
• Extract and summarize notable passages.
• Use NLP techniques to analyze the text for thematic patterns.
• Implement ChromaDB for efficient text retrieval and semantic search.

📊 Dataset Information

• Source: Turkish translation of the Chomsky-Foucault debate.
• Language: Turkish (input), English (analysis outcomes).
• Content: Philosophical discussions, linguistic theories, and sociopolitical perspectives.

🔬 Methodology

1️⃣ Data Preprocessing

• Extracted text from the book.
• Cleaned and formatted textual data for analysis.
• Translated relevant sections from Turkish to English for further processing.

2️⃣ Textual Analysis

• Identified recurring themes and key terms.
• Compared Chomsky’s linguistic and rationalist views with Foucault’s structuralist/post-structuralist perspectives.

3️⃣ NLP & Sentiment Analysis

• Applied Natural Language Processing (NLP) techniques to analyze the text structure.
• Measured sentiment trends within different sections.
• Utilized ChromaDB for efficient vector-based text retrieval and semantic search.
• Translated Turkish results into English for broader accessibility.

🛠 Technologies Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• NLP Libraries: NLTK, spaCy
• Database: ChromaDB for vector-based search and retrieval
• Translation: ChatGPT API
• Jupyter Notebook for interactive analysis

📌 Future Improvements

• Expand the analysis to other works by Chomsky and Foucault.
• Apply deep learning models for more nuanced text categorization.
• Improve translation accuracy and support bilingual analysis.
• Enhance ChromaDB implementations for more advanced text search capabilities.

🚀 How to Use

1. Clone the repository.
2. Install dependencies: pip install -r requirements.txt
3. Open the Jupyter Notebook and run the analysis cells.
4. Utilize ChromaDB to perform semantic searches on extracted text.
5. Review English-translated analysis outcomes.

📄 License

This project is intended for academic and research purposes only. Contributions and feedback are welcome!