We want to help people with psychological disorders that impair the ability to pick up social cues (i.e. schizophrenia, autism, social anxiety, ADHD, etc) and also English-language learners with an API that can determine whether a text statement is sarcastic or not. We decided on an API because we feel that this has multiple use cases, like a browser extension to check if forum posts are sarcastic, a phone app that checks how sarcastic your/others' texts are, or book reader app that highlights the sarcastic portions of a book.

What it does

Our API uses a Recurrent Neural Network combined with a statistical model using Bayesian statistics and word frequency to determine whether or not a sentence is sarcastic.

How we built it

We use Lua and the Torch library to train a neural network using sarcastic/non-sarcastic statements from a CSV file provided by the UCSC Natural Language and Dialogue Systems. After that, we made a REST API on a Ruby web server.

Challenges we ran into

  • Time and Processing Power - If we had weeks and better computers, we could train our neural network with more data sets and become more accurate with our results.

Accomplishments that we're proud of

  • Our first time making a neural network, and using the Torch library.
  • Achieving a better success rate with determining sarcasm than we expected.

What we learned

  • How to design and build a neural network

What's next for SassMaster

  • Reading context around statements to better determine sarcasm.
  • Training with more data sets to improve accuracy.

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