🎬 Enhanced YouTube Transcript Summarizer with Sentiment Analysis

🧠 Overview

This project is a powerful Streamlit-based web application that extracts and processes YouTube video transcripts using advanced Natural Language Processing (NLP) techniques.

It provides:

• ✂️ Summarization using Transformer models (T5)
• 🔑 Keyword Extraction with TF-IDF & fallback lemmatization
• 🧠 Topic Modeling using LDA
• 😊 Sentiment Analysis using both VADER and TextBlob
• 📥 Export options to download the results as .txt or .csv

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👨‍💻 Developer Info

• Full Name: Monpara Romil Kamleshbhai
• 🎓 B.Tech in Information Technology, LJIET (Graduating in 2027)
• GitHub: https://github.com/romilmonpara
• LinkedIn: https://www.linkedin.com/in/romilmonpara

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🚀 Features

• 🔗 Input any YouTube video URL
• 📝 Extracts and cleans transcript using youtube_transcript_api
• 🤖 Summarizes content via Hugging Face T5 Transformer model
• 🧹 Keyword extraction using TF-IDF with fallback to word frequency
• 📚 Topic modeling via LDA (scikit-learn)
• ❤️ Sentiment analysis using:
  • TextBlob (Polarity, Subjectivity)
  • NLTK's VADER (Positive, Negative, Neutral, Compound)
• 📊 Visual representation of sentiment results
• 📤 Downloadable results (TXT for summary, CSV for full data)

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🧰 Tech Stack

• Frontend: Streamlit
• Core Libraries:
  • youtube_transcript_api, pytube – YouTube integration
  • transformers – T5 summarization model
  • nltk, textblob – NLP, sentiment analysis
  • scikit-learn – Topic modeling (LDA)
  • matplotlib, pandas, base64 – Visuals & export
  • streamlit – UI & interaction

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⚙️ Setup Instructions

1. Clone the Repository

git clone https://github.com/romilmonpara/youtube-transcript-streamlit-ui.git

2. Install Required Libraries

Make sure you have Python 3.7+ installed.

pip install -r requirements.txt

3. Download NLTK Resources (First-time setup)

import nltk
nltk.download('punkt')
nltk.download('wordnet')
nltk.download('stopwords')
nltk.download('omw-1.4')
nltk.download('vader_lexicon')

4. Run the Streamlit App

streamlit run app.py

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🤖 How to Use

1. Open the app in your browser after launching Streamlit.
2. Paste a YouTube video URL in the input box.
3. Click "Analyze Video".
4. View:
   • Video metadata (title, author, views, etc.)
   • Raw transcript (optional)
   • Summary
   • Keywords
   • Topics
   • Sentiment plots
5. Download:
   • 📄 summary.txt
   • 📊 analysis.csv

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📌 Output Example

• Summary:

  "In this video, the speaker discusses..."

• Top Keywords:

  data science, machine learning, deep learning...

• Topics:

  Topic 1: ai, data, learning
  Topic 2: video, algorithm, streamlit

• Sentiment:

  Polarity: 0.15 | Subjectivity: 0.45
  VADER Compound Score: 0.74

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🛠️ Error Handling

• Transcript Not Available:

  The video must have closed captions enabled.

• Invalid URL:

  Only standard YouTube links are accepted.

• Model Error / CUDA Out of Memory:

  Reduce summary length or input shorter videos.

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🌟 Future Enhancements

• 🌐 Multilingual transcript support
• ⚡ Faster summarization with GPU model serving
• 🧠 Use more advanced models like BART or Pegasus
• 🖼️ Improve UI with themes and mobile responsiveness

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🙌 Acknowledgements

• Hugging Face Transformers
• NLTK & TextBlob teams
• Streamlit Community
• YouTube Transcript API developers

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⭐ Star this repo if you find it useful! ⭐

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