  Violence is an epidemic in Brazil and a problem on the rise world-wide.
Mobile devices provide communication technologies which can be used to monitor
and alert about violent situations. However, current solutions, like panic
buttons or safe words, might increase the loss of life in violent situations.
We propose an embedded artificial intelligence solution, using natural language
and speech processing technology, to silently alert someone who can help in
this situation. The corpus used contains 400 positive phrases and 800 negative
phrases, totaling 1,200 sentences which are classified using two well-known
extraction methods for natural language processing tasks: bag-of-words and word
embeddings and classified with a support vector machine. We describe the
proof-of-concept product in development with promising results, indicating a
path towards a commercial product. More importantly we show that model
improvements via word embeddings and data augmentation techniques provide an
intrinsically robust model. The final embedded solution also has a small
footprint of less than 10 MB.
