Predicting suicidal intent from social media text post using machine learning

Abstract

We are living in an age of modern science where cutting-edge technology has made the world so small. We can now easily connect with people worldwide via the internet and using social media. Social media has become a popular way to connect with people and to share our thoughts and feeling with the people we are connected. People are using social media as a tool where they share their feelings, daily life activities and so on. As a result, people are spending more times on those platforms to connect with people rather than in person. People who suffer from suicidal ideation are expressing their feelings and emotions on social platforms. As suicide is now an alarming problem in our society, we can use machine learning technology to determine suicidal ideation in the early stage based on social media data such as Twitter data and Reddit data. We have combined deep learning and an artificial neural network to make a model that we have named SIP (Suicidal Intent Prediction) which can detect suicidal ideation based on the text data of social media in the first place. In our proposed SIP model, we have used Functional, Word Embedding, Dense and GRU (Gated recurrent unit), Bi-directional LSTM, Bert to build our model. We have shown that our SIP model is able to determine the suicidal ideation with a higher training accuracy of 88%, a validation accuracy of 89% and training accuracy 98% and validation accuracy 99% from SIP (Sentiment) model.