Sprint 02 Update

• Collected relevant tweets using Pythonic library, Twint
• Cleaned and preprocessed the data by removing irrelevant information, standardizing text, and reducing dimensionality
• Labeled the sentiment using pre-trained sentiment analysis model, TextBlob
• Evaluated and refined the dataset by identifying mislabeled tweets, imbalanced data, and patterns/trends
• Stored the dataset in a PostgreSQL database management system for easy access and analysis
• Integrated the pipeline with Apache Airflow to automate the entire process and schedule it to run at regular intervals or specific events.