https://github.com/murapa96/youtubenlp
A Streamlit application for analyzing YouTube video comments using Natural Language Processing techniques.
https://github.com/murapa96/youtubenlp
comments nlp nltk sentimental youtube
Last synced: 3 months ago
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A Streamlit application for analyzing YouTube video comments using Natural Language Processing techniques.
- Host: GitHub
- URL: https://github.com/murapa96/youtubenlp
- Owner: murapa96
- License: gpl-3.0
- Created: 2023-03-08T17:34:34.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-11T19:07:07.000Z (3 months ago)
- Last Synced: 2025-03-11T20:22:51.170Z (3 months ago)
- Topics: comments, nlp, nltk, sentimental, youtube
- Language: Python
- Homepage:
- Size: 577 KB
- Stars: 15
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# YoutubeNLP
A Streamlit application for analyzing YouTube video comments using Natural Language Processing techniques.
## Screenshots
*Main Screen*
*Language Distribution*
*Sentimental Analysis*
## Features
- **YouTube Comment Analysis**: Automatically fetch and analyze comments from any YouTube video URL
- **Sentiment Analysis**: Evaluate the emotional tone of comments (positive, negative, or neutral)
- **Keyword Extraction**: Identify the most important words and topics in comments
- **Interactive Visualizations**:
- Sentiment distribution chart
- Comment length vs. sentiment scatter plot
- Interactive word cloud with sentiment filtering
- Top keywords bar chart
- Sentiment over time analysis (when available)
- Named entity recognition and analysis
- Language detection and language-specific insights
- **Data Export**: Download all analysis results as CSV for further research
## Requirements
- Python 3.7+
- Required packages listed in `requirements.txt`
- Optional: Google Cloud Language API credentials for enhanced NLP capabilities
## Installation
1. Clone this repository:
```bash
git clone https://github.com/murapa96/YoutubeNLP.git
cd YoutubeNLP
```
2. Run the setup script to install dependencies and download required models:
```bash
python setup.py
```
This script will:
- Install required packages from `requirements.txt`
- Download the necessary spaCy model using `python -m spacy download en_core_web_sm`
- Download required NLTK resources
- Set up necessary directories
- Check for Google Cloud credentials
3. (Optional) Set up Google Cloud Language API for enhanced analysis:
- Create a project in Google Cloud Console
- Enable the Cloud Natural Language API
- Create and download service account credentials
- Set the environment variable: `export GOOGLE_APPLICATION_CREDENTIALS=/path/to/credentials.json`
## Usage
1. Run the Streamlit app:
```bash
streamlit run app.py
```
2. Open your browser at the URL provided in the terminal (usually http://localhost:8501)
3. Enter a YouTube video URL and click "Analyze Comments"
4. Explore the interactive visualizations and insights
## Technical Information
- **Fallback Mechanisms**: Falls back to TextBlob for sentiment analysis if Google Cloud credentials aren't available
- **Multi-language Support**: Automatically detects language and applies appropriate analysis
- **Named Entity Recognition**: Identifies people, organizations, locations, and other entities in comments
- **Customizable**: Analysis options allow for comment count and sorting preferences
- **Efficient Processing**: Uses batched processing for better performance with large comment volumes
## Credits
Created by [Pablo Ramos Muras](https://github.com/murapa96)
## License
This project is licensed under the terms of the license included in the repository.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.