Sentiment Analysis of Serious Suicide References in Twitter Social Network

Sentiment analysis analyzes people emotions, attitudes, and opinion towards organizations, services, issues, and individuals. Opinions are the core of almost all human activities because they consider a significant influencers of our behaviors. With the growing popularity of social media applications (micro-blogs, twitter, comments, etc), users of these platforms express their emotions through their posts and comments. Suicide is one of these dangerous emotions that threaten the public health of Canadians, and mortality form suicide is the third leading cause of death in teenage. In this paper, we propose a suicide classifier system called Auto Twitter Suicide Detector System (ATSDS) that provides support to authorities to take appropriate actions in order to protect communities from such kind of thoughts. The proposed twitter suicide detector system is a classifier system using data gathered from twitter to detect those related to suicide. Our system is built using deep neural network on multi-purpose cluster computing system called spark. In order to asses the system performance, in terms of accuracy, we have conducted several experiments and tuned neural network parameters to achieve higher performance. The results returned are very promising.