Youtube-Comment-Analyzer-and-Summarizer

The YouTube Comment Analyzer is a powerful tool designed to analyze and extract insights from user-generated comments on YouTube. Leveraging advanced Natural Language Processing (NLP) techniques, this project offers functionalities such as sentiment analysis, comment extraction, real-time monitoring, and summary generation.

Key Features

• Sentiment Analysis: Utilizes NLP techniques to assess the emotional tone of YouTube comments, providing valuable insights into viewer sentiment towards specific videos or topics.

• Comment Extraction: Efficiently extracts comments from YouTube videos, enabling content creators, researchers, and marketers to understand audience engagement.

• Real-Time Monitoring: Offers real-time monitoring capabilities, allowing users to track changes in comment sentiment over time and react promptly to emerging trends.
• Summary Generation: Employs extractive summarization techniques to condense large volumes of comments into concise summaries, facilitating quick comprehension of key points and sentiments expressed.

• Advanced NLP Techniques: Utilizes techniques such as tokenization, named entity recognition, and part-of-speech tagging to enhance the accuracy and depth of comment analysis.
• User-Friendly Interface: Features a user-friendly interface that makes the tool accessible to a wide range of users, including non-technical individuals, content creators, and marketers.

Installation

To run the YouTube Comment Analyzer locally, you can clone the repository to your local machine and run the application python app.py.

Usage

Once the application is running, you can interact with it by accessing the provided endpoints or using the user interface. Here are some examples of how to use the functionalities:

• Perform sentiment analysis on YouTube comments: [POST] /sentiment-analysis
• Extract comments from a YouTube video: [GET] /extract-comments?video_id=<video_id>
• Generate a summary of comments: [POST] /generate-summary

For more detailed usage instructions, refer to the documentation or the provided examples in the codebase.

Technologies Used

• Python
• Flask
• HTML/CSS
• JavaScript
• NLP libraries (e.g., NLTK, spaCy)
• Other dependencies listed in requirements.txt

Contributing

Contributions to the YouTube Comment Analyzer are welcome! To contribute, follow these steps:

1. Fork the repository
2. Create a new branch
3. Make your changes
4. Submit a pull request

Please ensure that your code follows the project's coding standards and includes appropriate documentation.

License

This project is licensed under the MIT License.

Reference

For more references you can refer our research paper.

Acknowledgments

I would like to thank Pratik Mane, Pratik Mandalkar, Netra Mohekar, Prajakta Kumbhare for their contributions to this project.