YouTube Sentiment Analysis 😊😐😔😡

About

A web application that performs sentiment analysis on YouTube video comments. The application uses the YouTube Data API to fetch comments and then analyzes their sentiment using a pre-trained model. Results are visualized and displayed on a web interface.

Sentiment Analysis is a popular application of Natural Language Processing (NLP). This project focuses on analyzing YouTube comments to understand the sentiments expressed by viewers.

Live Link

Hosted on GitHub Pages

🔗 Live Demo Link

Project Specifications

Below are the libraries and frameworks used to create the project:

• Web Framework: Flask
• Visualization: Matplotlib
• Sentiment Analysis Libraries: TensorFlow/Keras
• API Requests: requests

Project Components

The project currently includes:

1. Comment Analysis - Fetches comments from a YouTube video using the YouTube Data API and analyzes their sentiment.
2. Visualization - Displays sentiment distribution and insights using charts.

Screenshots

Application Interface

Sentiment Analysis Results

Important Information

YouTube Data API

API documentation link - YouTube Data API Documentation

To work with the API, you need to create an API key. To create an API key, register on the Google Cloud Console and a unique key will be generated for you. Use this key to make successful API requests.

Note: Ensure your API key is kept secure and adhere to usage limits.

API Specifications

To fetch comments, the application performs the following API calls:

1. Fetch Comments - Retrieve comments for a YouTube video using the YouTube Data API.
2. Analyze Sentiment - Process the comments and analyze their sentiment.

API Endpoint for Comments - https://www.googleapis.com/youtube/v3/commentThreads?part=snippet&videoId={videoId}&key={apiKey}

Models Used

The project uses pre-trained models for sentiment analysis. Here’s a brief overview of the tools used:

• TensorFlow/Keras - Libraries for building and using machine learning models for sentiment analysis.

Note:

1. The sentiment analysis model classifies comments into sentiment categories (POSITIVE🙂, NEGATIVE☹️, NEUTRAL😐).

Project Development Ideas

Future enhancements may include:

• Analyzing comments in different languages.
• Integrating with other social media platforms.
• Enhancing visualizations with interactive charts.

Thank You!

Thank you for exploring the project. I hope you find it useful.

If you did, please consider giving a star⭐ to this repository. It would mean a lot!

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© 2024 Rajeev Sharma | [email protected]