A Cost-aware Study of Depression Language on Social Media using Topic
  and Affect Contextualization
Andrea Laguna Oscar Araque
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.