Enhancing Depression Detection from Narrative Interviews Using Language Models

In recent years, the prevalence of mental health issues among young people has significantly increased, especially due to the consequences of COVID-19 pandemic and the widespread adoption of remote work arrangements. Depression has emerged as a major global mental health concern due to its potentially devastating consequences. However, existing methods for depression detection still face several challenges, such as limited data availability, imbalanced labels, and inadequate consideration of contextual information. To tackle these challenges, in this paper, we first create a larger dataset, namely I-DAIC, for depression detection by integrating three existing datasets in the literature. We further fine-tune and examine two pre-trained transformer-based language models by comparing them with two traditional machine learning methods on the I-DAIC dataset. To overcome the difficulty of handling lengthy texts, we explore several customized strategies in combination with the advanced language models. Moreover, we conducted the quantitative analysis of key representative keywords using topic modeling for both non-depression and depression instances. The comprehensive experimental results demonstrated the effectiveness, advantages, and significant potential of pre-trained language models for depression detection with narrative interviews.