Comparative evaluation of deep dense sequential and deep dense transfer learning models for suicidal emotion prediction

Abstract

SummaryIn today's world, there is a lot of anxiety about suicidal thoughts that is conveyed on social media platforms, and people are now sharing all kinds of feelings on social media forums. The majority of them utilize social forums because they feel uncomfortable sharing privately. The study's objective is to analyze suicidal thoughts and identify them at an early stage by utilizing deep learning and transfer learning techniques. These algorithms are used to data gathered from users of Reddit forums who have suicidal thoughts as well as regular users who have non‐suicidal thoughts. For the aforementioned goal, we use techniques such as the transfer learning algorithms BERT, RoBERTa, and ALBERT, as well as BiLSTM and other deep learning algorithms. In contrast to the sequence processing model, our study demonstrates that the bidirectional long‐short term algorithm provides the best validation accuracy, while the pretrained models BERT and ALBERT also provide satisfactory accuracy.