Topic Modeling in Embedding Spaces for Depression Assessment

Abstract

This paper presents an investigation of topic modeling in embedding spaces performances in the context of depression assessment. Using the textual content of social media users from the eRisk 2018 dataset, a classification task is performed employing features generated from the Embedded Topic Model. To set contrast with traditional topic modeling, a full comparison with the Latent Dirichlet Allocation model is shown. An extensive range of topics and different preprocessing strategies are studied to demonstrate the efficiency of the models. Our results show a noteworthy improvement in the explored task from the application of the novel topic modeling approach.