Etika, a famous Youtuber, committed suicide last year. There were many signs of his struggle with depression through his social media posts. If these signs could have been analyzed more carefully and effectively, more people may have been able to help Etika during his time of need.
What it does
The program analyzes social media posts and the language that is used in said post. It takes into account the word choice and attempts to predict or inference the poster's mood and mental state.
How I built it
Microsoft Azure provided an API for language mood detection. Using this API, I constructed a means of converting text (in this case a tweet) into a numerical value that informs us of the poster's mental state.
Challenges I ran into
I had a difficult time integrating the Microsoft Azure API. This was because I had to utilize postman's and other various methods to get the whole system working in a single python environment
Accomplishments that I'm proud of
What I learned
Microsoft Azure is a great service to find tools that already exist, saving time from developing our own but redundant programs.
What's next for Preemptive Homicide Prevention
Ideally, Twitter incorporates this program. However, baby steps before that occurs, we would need PHP to automatically gather social media posts as inputs, without human interaction.