Depression is a growing issue in society's mental health that affects all areas of life and can even lead to suicide. Fortunately, prevention programs can be effective in its treatment. In this context, this work proposes an automatic system for detecting depression on social media based on machine learning and natural language processing methods. This paper presents the following contributions: (i) an ensemble learning system that combines several types of text representations for depression detection, including recent advances in the field; (ii) a contextualization schema through topic and affective information; (iii) an analysis of models' energy consumption, establishing a trade-off between classification performance and overall computational costs. To assess the proposed models' effectiveness, a thorough evaluation is performed in two datasets that model depressive text. Experiments indicate that the proposed contextualization strategies can improve the classification and that approaches that use Transformers can improve the overall F-score by 2% while augmenting the energy cost a hundred times. Finally, this work paves the way for future energy-wise systems by considering both the performance classification and the energy consumption.

翻译：抑郁症是影响社会心理健康的日益严重的问题，它波及生活的各个领域，甚至可能导致自杀。幸运的是，预防项目在其治疗中可发挥有效作用。在此背景下，本研究提出一种基于机器学习与自然语言处理方法的社交媒体抑郁检测自动化系统。本文贡献如下：(i) 一种集成学习系统，该系统结合了多种文本表示方法用于抑郁检测，涵盖该领域最新进展；(ii) 一种通过主题与情感信息进行语境化的框架；(iii) 对模型能耗的分析，建立了分类性能与总体计算成本之间的权衡。为评估所提模型的有效性，在两个模拟抑郁文本的数据集上进行了全面评估。实验表明，所提出的语境化策略能够改善分类效果，而使用Transformer的方法可将整体F值提升2%，同时将能耗成本提高百倍。最后，本研究通过综合考虑分类性能与能耗，为未来节能型系统的开发铺平了道路。