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This project uses Twitter API v2 with Tweepy to request tweet data based on a keyword, the data is then put through an ETL process, and into Pandas data frame, and then run through a pre-trained sentiment analysis model. The sentiment result is then added to the tweets and the resulting table is used for analysis.
This is End-To-End Data Engineering Project using Airflow and Python. In this project, we will extract data using Twitter API, use python to transform data, deploy the code on Airflow/EC2 and save the final result on Amazon S3
The dataset which will be wrangling (and analyzing and visualizing) is the tweet archive of Twitter user @dog_rates, also known as WeRateDogs. WeRateDogs is a Twitter account that rates people's dogs with a humorous comment about the dog. I will use Python (and its libraries) to analyze and visualize the dataset through Jupiter notebook.
This project utilizes Twitter API and Kafka to monitor keywords from Twitter and make a sentiment analysis. Developed using Django, ReactJS, MongoDB, Docker, Kafka and Zookeeper.