Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/machphy/youtube_sentiment_analysis_api
analyzes sentiment in YouTube video comments using the YouTube Data API and a pre-trained model. It fetches comments, performs sentiment analysis, and visualizes the results on a web interface.
https://github.com/machphy/youtube_sentiment_analysis_api
api flask gcp jinja2 keras matplotlib python requests tensorflow
Last synced: 2 days ago
JSON representation
analyzes sentiment in YouTube video comments using the YouTube Data API and a pre-trained model. It fetches comments, performs sentiment analysis, and visualizes the results on a web interface.
- Host: GitHub
- URL: https://github.com/machphy/youtube_sentiment_analysis_api
- Owner: machphy
- Created: 2024-07-26T14:02:56.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-11-29T07:17:35.000Z (about 2 months ago)
- Last Synced: 2024-11-29T08:19:58.667Z (about 2 months ago)
- Topics: api, flask, gcp, jinja2, keras, matplotlib, python, requests, tensorflow
- Language: Python
- Homepage:
- Size: 15.2 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 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](https://github.com/machphy/youtube_sentiment_analysis_API)
## 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](https://developers.google.com/youtube/v3/docs)
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!
---
© 2024 Rajeev Sharma | [[email protected]](mailto:[email protected])