Keyword_extraction_using_NLP

This project demonstrates how to extract keywords from raw text using three popular NLP techniques:

• 🔢 TF-IDF (Term Frequency-Inverse Document Frequency)
• 🧠 RAKE (Rapid Automatic Keyword Extraction)
• 🔗 TextRank (Graph-based ranking algorithm)

Developed entirely in Google Colab using Python and nltk, rake_nltk, sklearn, and networkx.

🚀 Project Objective

To compare different keyword extraction methods and understand how each performs on a sample corpus of text. This is useful for applications in:

• Search engine optimization
• Summarization tools
• Content classification
• Information retrieval

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📊 Methods & Workflow

1. Data Input: Raw text string (manually input).
2. Preprocessing: Tokenization, stopword removal.
3. TF-IDF: Extracts top words based on statistical frequency.
4. RAKE: Extracts keyword phrases based on word co-occurrence and frequency.
5. TextRank: Builds a graph of words and uses PageRank to find the most relevant ones.

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🛠️ Libraries Used

• nltk
• rake_nltk
• sklearn
• networkx
• matplotlib

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📓 Notebook

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✨ Output

Each method extracts a ranked list of keywords from the same input text.
This helps visually and practically compare how different techniques interpret "importance."

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