The team was inspired by the Twitter mood but wanted to make it more powerful.

What it does

Our product allows users to specify topics of interest then we analyze the popularity, overall sentiment, and compare related topics.

How we built it

We began by defining the separations between the various components. Then we set off to work on our respective components.

Challenges we ran into

Performance of the natural language processing tools we're initially unusable. However, we were able to optimize its performance using several clever tricks.

Fitting the various components together was a real challenge, due to several necessary tools being implemented in different programming languages. However, the team overcame it by interprocess communication.

Accomplishments that we're proud of

Delivering a well polished front end experience on top of a powerful backend.

What we learned

The team learned d3.js as well as the twitter API. The team learned the core concepts of natural language processing.

What's next for Open Opinion

Further performance optimizations through custom natural language processing models.

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