Effects of Extended Features on BERT Performance: Depression Detection

In this study, the effects of categorical and numerical additional features obtained from Twitter posts on depression detection were investigated. Depression detection performances of the BERT large language model and SVM classifier were compared on the dataset balanced with the oversampling method. The effects of two different feature addition methods, Unimodal and Concat, were evaluated on the BERT model. The results show that oversampling improves the performance of the BERT classifier, but feature addition methods do not provide significant improvement in the model performance. The findings of the experiments reveal the success of the BERT model in the field of classification and that it does not require additional features for the detection of depression. It is believed that this study will guide research in the field of depression detection and help researchers identify more effective areas of study.