  This paper presents the Deep Bag-of-Sub-Emotions (DeepBoSE), a novel deep
learning model for depression detection in social media. The model is
formulated such that it internally computes a differentiable Bag-of-Features
(BoF) representation that incorporates emotional information. This is achieved
by a reinterpretation of classical weighting schemes like term
frequency-inverse document frequency into probabilistic deep learning
operations. An important advantage of the proposed method is that it can be
trained under the transfer learning paradigm, which is useful to enhance
conventional BoF models that cannot be directly integrated into deep learning
architectures. Experiments were performed in the eRisk17 and eRisk18 datasets
for the depression detection task; results show that DeepBoSE outperforms
conventional BoF representations and it is competitive with the state of the
art, achieving a F1-score over the positive class of 0.64 in eRisk17 and 0.65
in eRisk18.
