Automated Data Pipeline for Social Media Sentiment Analysis

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.