Suicide prediction on social media by implementing sentiment analysis along with machine learning

Abstract

Technology is growing day by day and the influence of them on our day-to-day life is reaching new heights in the digitized world. Most of the people are prone to the use of social media and even minute details are getting posted every second. Some even go to the extent of posting even suicide related issues. This paper addresses the issue of suicide and is predicting the suicide issues on social media and their semantic analysis. With the help of Machine Learning techniques and semantic analysis of sentiments the prediction and classification of suicide is done. The model of approach is a four-tier approach, which is very beneficial as it uses the twitter4J data by using weka tool and implementing it on WordNet. The precision and accuracy aspects are verified as the parameters for the performance efficiency of the procedure. We also give a solution for the lack of resources regarding the terminological resources by providing a phase for the generation of records of vocabulary also.