First we realized that election polling is archaic. Then we realized that mass polling in general, such as in a college campus, is also weak and inconsistent. Finally, we decided to build a product to make polling smooth, non-intrusive, and potentially significantly more accurate and insightful.

What it does

Users can join polling channels by scanning QR codes. For example, Wash U students can scan the Wash U QR code to gain entry into the Wash U polling channel. When a question is posed to the community, all recipients within the respective channel will get the question via SMS. If inclined to do so, the citizens can reply not only with their answers, but also with their explanations and opinions. Our backend parses through all the text messages, and returns a succinct summary of the citizens' average general attitude towards the topic, in addition to popular keywords and phrases. For example, if the question "Should we bring back Motz Sticks?" was asked to Wash U's channel, the summary of all the responses would read something like "The attitude towards Motz Sticks is ecstatic." Not only does our product provide the most convenient way of polling citizens at any scale, but it also provides extremely powerful data analysis that converts the results in succinct summaries.

How we built it

iOS, Twilio, IBM Alchemy

Challenges I ran into

Hooking up all the multiple backend modules was a pain. Documentation and online support for Alchemy was not the strongest. The backend of the project is huge, so naturally it was the sources of most of our challenges.

Accomplishments that we're proud of

Getting data analysis to work and summary creation to work. Also the "joining communities via QR code" was pretty cool.

What we learned

Backend is hard. Twilio is hard.

What's next for

Test out in a residential college to help us find what events and activities people want.

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