To develop an automated data engineering pipeline that extracts, processes, and analyzes social media data for sentiment classification. The system aims to support business decision-making by transforming raw social data into structured analytical insights.
Study APIs for social media data extraction.
Collect tweets or posts using API integration.
Design a data ingestion workflow using Python and Kafka.
Clean and preprocess textual data for analysis.
Store raw data in a data lake environment.
Perform transformation using Spark for large-scale text processing.
Implement sentiment analysis using machine learning models.
Store processed results in a structured database.
Create dashboards showing sentiment trends.
Automate pipeline scheduling with workflow tools.
Optimize processing time and resource usage.
Handle missing and noisy data efficiently.
Implement data validation checks.
Conduct a performance evaluation of the system.
Prepare final project documentation and presentation.
