Inspiration
Due to the COVID-19 Pandemic, many have increased mental stress due to Trauma from widespread disease, financial concerns, and even Grief over losses of life. In fact, 40% of individuals surveyed from the CDC stated that they were feeling depressed as a result of this pandemic. Moreover, this large population of anxious and depressed individuals has a lack of motivation to ask for help, an effect that the energy-draining symptoms of depression can cause. Specifically, People that are depressed do not have the energy to manually contact their therapists for help through text and call.
What it does
Individuals can journal to themselves as a form of self-reflection; the journal will then use NLP in tandem with sentiment analysis to detect the range of emotions, and store the sentiment value in a database for easy access for visualization and sharing purposes. The user can then decide whether they are interested in easily sharing their mood levels with their therapist with the click of a button, protecting the user's personal journals while still sharing facts about their wellbeing to their therapists, prompting action if need be. This will allow therapists, for example, see trends of negative data, and then use that data to start a plan to help the client.
How we built it
We utilized local storage for storing login information. We used this login information to grab journal entries as well as sentiment scores, from our Google Cloud database through our verification algorithms. We then submit information to therapists using stored phone numbers in local storage and Twilio, from our Google Cloud Database. Furthermore, our C3.JS data visualization tool extracts data from our Google Cloud Database and displays it based on the sentiment score, allowing users to see how positive/negative/neutral they are in their journal entries.
Challenges we ran into
We initially didn't know how to use local storage; we were trying to transfer data between pages using socket.io, but that doesn't work since new page's are different sockets. Once we learned how to use local storage, we were able to have persistent login data to verify our users and only show their information.
Accomplishments that we're proud of
Using local storage and Google cloud in combination was very helpful for verification purposes; using the local storage to have access to the username and passwords, and then creating an algorithm to compare the local storage username and password with the data entries in the Google Cloud's username and password allowed us to make an amazing authentication system. As a result, we are able to allow any user to sign up and only see their data. Using c3.js for visualization was amazing to allow users to easily see their trends for their sentiment scores.
What we learned
Using local storage for persistent data, along with Google Cloud in combination was a transformative experience for creating authentication systems. Using c3.js for visualization was beautiful as well.
What's next for Mentis.
Native app implementation.
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