BrandIt

Inspiration

We wanted to build a business-centric application that would fill many services.

What it does

BrandIt searches the last 100 tweets posted on twitter with a specific hashtag. These tweets are put through an algorithm that categorizes them into positive and negative tweets based on the words used within. It then updates the web app with numbers, percentages, and graphs that relate to the "positive reviews" that hashtag is receiving through twitter.

How we built it

We used an API called Tweepy to connect with the twitter API. This allowed us to get a list of the 100 most recent tweets that share the given hashtag. It then goes through a few functions of string manipulation to clean the tweets up into string arrays that we can compare against our word lists. We count the number of tweets that contain more "bad" and "good" words and then push those numbers back onto the webpage to give the user the information.

Challenges we ran into

The main challenge we faced was having to learn new programming languages and APIs. It was especially difficult finding a way to locally host a webpage that could run a python app that connects to the HTML.

Accomplishments that we're proud of

We are proud of the amount of polish we were able to put on the user interface after having to spend most of our time working in the backend. Being able to learn the skills required (flask especially) for this project was also very rewarding.

What we learned

We learned how to make python interact with HTML and javascript. We also learned a little bit about how the Twitter API works.

What's next for BrandIt

There are some other things that we could think about going forward, especially when it comes to some of the functionalities of the twitter API that we did not have the time to use such as geotag data.