MySafetyNet is an app that help you express your feelings and keeps track of your daily mood, with the objective of making you feel better, as well as giving you the means to receive help if needed. It can also be used by psychologist or psychiatrist to get a better record on their patients.
MySafetyNet performs sentiment analysis based on Natural Language Processing (NLP) to help translate your answers into data that we can analyze and track. Using Tensorflow, we were able to import the wide known NLP model BERT in its mobile version, and proceeded to perform transfer learning with a database ready to perform sentiment analysis. This model was later implemented in an iOS app using XCode.
Since this is a delicate topic and we dont have much time for research, we found the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), where we could find information from the American Psychiatric Association and questions made in real cases which help to make a diagnosis.
Since this is a very short-time proyect and its touching critical aspects of people's life and health, we would like to review the questions asked, as well as the analysis done from the answers, which require both medical supervission and better learning methods that can decide how to assess the person in a more sensible way.
| Name | |
|---|---|
| Abraham Collins | A01283166@tec.mx |
| Ankush Kesri | kesri.a@northeastern.edu |
| Enrique Mondragon | A01366000@tec.com |
| Erick Cuellar | A01383577@tec.mx |