I was inspired by the stories of multiple successful entrepreneurs on twitter

What it does

Tweetvibe analyses the direct replies of a tweet and performs sentiment analysis on them to get the reply vibe

How we built it

Tweetvibe is built using python (flask, twitter api, google natural language processing api) in the backend with nim (karax) and chart.js in the frontend

Challenges we ran into

I ran into challenges on improving the server speed and on the issue of not being able to get historic tweets

Accomplishments that we're proud of

I am proud of tweetvibe as a whole.

What we learned

I learn't a lot about how to run a social media business while researching for ideas

What's next for TweetVibe

I aim for make tweetvibe more robust so that it can analyse more accurately the sentiment or a tweets replies

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