Transformer Based Approach for Depression Detection

Abstract

Mental health of a person plays equivalent significant role in ensuring their wellbeing as their physical health. A great deal of work and e ffort has gone into increasing awareness of this issue. One su ch effort is made by the discipline of computer science, whic h makes use of social media data to give more information in identifying these mental illnesses. People are increasingly usi ng internet platforms to voice our suicide ideas as technology advances quickly. The purpose of the study is to identify a person's indicators of depression based on their social media postings, where users express their feelings and emotions. The goal of this study is to develop three models-Naive Bayes, Pre-Trained Model BERT, and XLNET-and compare their performance in identifying depression from messages on Twitter. These models are pre-processed using the Tweet preprocessor and BERT embeddings, and then the pretrained models are fine-tuned. With an accuracy of 0.9942, it was found that Bert performed better than the other two models.