A Novel Optimizer Technique for Suicide Prediction In Twitter Environment

Today, online social media is ubiquitous, especially as it has become essential to all aspects of our daily lives and has been strongly linked to depression and suicidal tendencies. Here, we are interested in the changing emotional states of individuals captured via microblogging services such as Twitter. Our goal was to optimize the machine learning model using GW Life GWO to detect a person in a suicide situation by analyzing the social network Twitter. The objective is to predict the onset of suicidal ideation. Experimental work verify the effectiveness of our approach in terms of recall and precision to detect suicidal profiles.