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COVID19 Twitter Sentiment Analysis

This project was done in Jupyter Notebook. The file has been uploaded to the repo

BIG ISSUE YOU SHOULD KNOW

When you open the ipynb file, it might take a while to load, and scrolling is going to reveal a blank page. This is because I was somewhat unintelligent in my function calls and one of them literally prints out every token in every line of every tweet, so basically 99% of the page is just that one line executing. Please bear with it, as there are nice graphs and visuals above and below this huge useless line of text. Thank you!

Implementation and Details

This project consists of 4 parts: -Text Preprocessing -Sentiment Analysis -KeyWord Extraction -Entity Extraction

All of them are in the code, as well the live-runtime results.

What I learned

This was my first major project in Jupyter. I really like the design and feel of having an uploading and modern workbook as opposed to something more traditional such as VScode. All the models are really clean which made this project a super visual and interactive endeavor. I used lots of new libraries and modules that are not only helpful for visualizing data but also parsing Tweets and making them presentable data structures.

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