Detection of the Severity Level of Depression Signs in Text Combining a Feature-Based Framework with Distributional Representations

Author:

Muñoz Sergio1ORCID,Iglesias Carlos Á.1ORCID

Affiliation:

1. Intelligent Systems Group, Telematic Systems Engineering Department, Universidad Politécnica de Madrid, Avenida Complutense 30, 28040 Madrid, Spain

Abstract

Depression is a common and debilitating mental illness affecting millions of individuals, diminishing their quality of life and overall well-being. The increasing prevalence of mental health disorders has underscored the need for innovative approaches to detect and address depression. In this context, text analysis has emerged as a promising avenue. Novel solutions for text-based depression detection commonly rely on deep neural networks or transformer-based models. Although these approaches have yielded impressive results, they often come with inherent limitations, such as substantial computational requirements or a lack of interpretability. This work aims to bridge the gap between substantial performance and practicality in the detection of depression signs within digital content. To this end, we introduce a comprehensive feature framework that integrates linguistic signals, emotional expressions, and cognitive patterns. The combination of this framework with distributional representations contributes to fostering the understanding of language patterns indicative of depression and provides a deeper grasp of contextual nuances. We exploit this combination using traditional machine learning methods in an effort to yield substantial performance without compromising interpretability and computational efficiency. The performance and generalizability of our approach have been assessed through experimentation using multiple publicly available English datasets. The results demonstrate that our method yields throughput on par with more complex and resource-intensive solutions, achieving F1-scores above 70%. This accomplishment is notable, as the proposed method simultaneously preserves the virtues of simplicity, interpretability, and reduced computational overhead. In summary, the findings of this research contribute to the field by offering an accessible and scalable solution for the detection of depression in real-world scenarios.

Funder

MIRATAR project

Spanish Ministry of Science and Innovation

Spanish Ministry of Economic Affairs and Digital Transformation

Recovery, Transformation and Resilience Plan

European Union NextGeneration EU funds

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference75 articles.

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Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection;Big Data and Cognitive Computing;2024-09-05

2. BERT-based RNN for Effective Detection of Depression with Severity Levels from Text Data;2024 IEEE Symposium on Wireless Technology & Applications (ISWTA);2024-07-20

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