A Hybrid Deep Learning Model Using Grid Search and Cross-Validation for Effective Classification and Prediction of Suicidal Ideation from Social Network Data.

Abstract

Suicide deaths due to depression and mental stress are growing rapidly at an alarming rate. People freely express their feelings and emotions on social network sites while they feel hesitant to express such feelings during face-to-face interactions with their dear ones. In this study, a dataset comprising 20,000 posts was taken from Reddit and preprocessed into tokens using a variety of effective word2vec techniques. A new hybrid approach is proposed by combining the attention model in a convolutional neural network and long-short-term- memory. The objective of this research is to develop an effective learning model to evaluate the data on social media for the efficient and accurate identification of people with suicidal ideation. The proposed attention convolution long short-term memory (ACL) model uses hyperparameter tuning using a grid search to select optimized hyperparameters. From the experimental evaluation, it is shown that the proposed model, that is, ACL with Glove embedding after hyperparameter tuning gives the highest Accuracy of 88.48%, Precision of 87.36%, F1 score of 90.82% and specificity of 79.23% and ACL with Random embedding gives the highest Recall of 94.94% when compared to the state-of-the-art algorithms.