Detecting Suicide Risk in Chat Applications
In this report we built a system to detect urgent, high-risk messages in a chat application connecting its users to psychologists. With a group of volunteering psychologists, we categorised segment pairs of conversations into high-risk messages that require urgent attention and normal messages. We annotated 1795 examples out of which 612 are emergencies. We were thus facing an imbalanced classification problem.