Having been fed up with spam email and inspired by SafeTrek's mission, we decided to combine the two into something we were passionate about: improving the safety of others through an appropriate emergency dispatch and threat assessment based on filtering key words in a user's text message.
What it does:
1) User enters a text message stating that he/she is in danger
2) User presses a button to submit his/her typed sentence into our application
3) Application takes the sentence, and splits it based on space delimiter
4) Application function takes in parameter of threshold, and runs each word through our Naive Bayes algorithm:
a) Remove stop words such as "and" "of" "the"
b) Reduce words to distinct ones (remove duplicate words)
c) Stemming (remove past tense, plurals, etc)
5) Calculate probability that user is in danger for each word
6) Multiply all probabilities together, compare with threshold parameter, and return boolean of whether user is in danger and needs an emergency dispatch
How we built it:
Challenges we ran into:
Accomplishments that we're proud of:
Creating a working prototype by demo day and overcoming the challenges that stumped us for hours on end.
What we learned:
What's next for Naive Bayes Interpreter:
Incorporating all the words in the English-Dictionary to be used with the Naive Bayes algorithm, in addition to streamlining the project to be a full-fledged independent web application.