  This paper describes our participation in the MentalRiskES task at IberLEF
2023. The task involved predicting the likelihood of an individual experiencing
depression based on their social media activity. The dataset consisted of
conversations from 175 Telegram users, each labeled according to their evidence
of suffering from the disorder. We used a combination of traditional machine
learning and deep learning techniques to solve four predictive subtasks: binary
classification, simple regression, multiclass classification, and multi-output
regression.
  We approached this by training a model to solve the multi-output regression
case and then transforming the predictions to work for the other three
subtasks.
  We compare the performance of two modeling approaches: fine-tuning a
BERT-based model directly for the task or using its embeddings as inputs to a
linear regressor, with the latter yielding better results. The code to
reproduce our results can be found at:
https://github.com/simonsanvil/EarlyDepression-MentalRiskES
