A scalable ETL pipeline designed to extract, process, and analyze sentiment from YouTube comments in real-time, using Apache Kafka, Apache Spark, Python NLP, MongoDB, and visualized through Tableau, with orchestration managed by Apache Airflow and containerized via Docker.
Here’s an overview of the pipeline process:
Data Extraction: The YouTube Data API is used to automatically fetch comments from specific videos or channels in real-time. This ensures a continuous flow of fresh data into the pipeline for analysis.
Data Streaming: Apache Kafka handles the real-time transmission of comments, streaming them through the system for processing. Kafka ensures that the data flows smoothly, even as the volume of comments grows.
Data Processing: Apache Spark processes the incoming comment data, performing necessary transformations such as cleaning and filtering out irrelevant information. It prepares the data for sentiment analysis using its distributed processing capabilities.
Sentiment Analysis: Python’s natural language processing libraries analyze each comment, classifying them as positive, negative, or neutral. This step provides the core insights into how audiences feel about the content.
Data Storage: Once analyzed, the comments and their associated sentiment scores are stored in MongoDB, a flexible NoSQL database that accommodates unstructured data. MongoDB allows for efficient querying and retrieval of results.
Workflow Orchestration: Apache Airflow orchestrates the entire process, ensuring that tasks are executed in the correct order, monitoring progress, and handling retries in case of failures.
Data Visualization: The final step involves using Tableau to create visualizations based on the sentiment data. Stakeholders can explore trends and patterns in audience reactions through interactive dashboards.
This pipeline ensures a seamless flow from comment extraction to sentiment analysis and visualization, offering an efficient and scalable solution for understanding audience sentiment on YouTube.