This project performs real-time sentiment analysis on YouTube comments using Kafka, Spark, Docker, and Streamlit. It processes YouTube comments to determine the sentiment (positive, neutral, or negative) and visualizes the results in a web interface.

• Installation
• Project Structure
• Technologies Used
• License

Before you begin, ensure you have the following installed:

• Docker
• Python 3.9
• Streamlit

1. Clone the repository:

   git clone https://github.com/C0HEr/YTComments.git
   cd YTComments
   

2. Pull the Docker Hub image:: You can use the pre-built Docker image available on Docker Hub:

   docker pull d3v3r/ytcomments
   
   

3. Build and start Docker containers: The project uses Docker to manage services like Kafka, Spark. Use the following command to start them:

   docker-compose up --build
   
   This command will:
   - Build the necessary Docker images for your services.
   - Start the Kafka, Spark, and Streamlit services
   
   After the services start, you can access the Streamlit dashboard at http://localhost:8501.
   

4. Project Structure:

   ├── app/
   │   ├── spark.py              # Spark job for processing comments and performing sentiment analysis
   │   ├── kafka.py              # Kafka producer and consumer setup
   ├── dashboard.py              # Streamlit dashboard for visualizing sentiment analysis results
   ├── docker-compose.yml        # Docker configuration for Kafka, Spark, Zookeeper, and Streamlit
   ├── Dockerfile                # Dockerfile for building the Streamlit app container
   ├── requirements.txt          # Python dependencies
   ├── README.md                 # Project documentation (this file)

• app/spark.py: Contains the Spark logic for reading from Kafka, processing the comments, and performing sentiment analysis.
• app/kafka.py: Kafka producer/consumer logic for handling comment streams.
• dashboard.py: The Streamlit application that visualizes the processed sentiment data.
• docker-compose.yml: Defines the multi-container setup for Kafka, Spark, Zookeeper, and Streamlit services.
• Dockerfile: Builds the Docker image for the Streamlit application.
• requirements.txt: Lists the Python libraries needed for the project.

• Kafka: Message broker for real-time comment ingestion.
• Apache Spark: For processing and analyzing YouTube comments.
• Streamlit: To create an interactive web dashboard to display sentiment analysis results.
• Docker: To containerize all the services and make deployment easier.

You can find the Docker image for this project on Docker Hub:

- **Image Name**: [d3v3r/ytcomments](https://hub.docker.com/r/d3v3r/ytcomments)

You can pull the image using the following command:

```bash
docker pull d3v3r/ytcomments