## 5 Conclusions and Future Work  
This paper presented DEENT, an approach based on encoder-only Transformer architecture to detect depressive and non-depressive tweets. The proposed approach introduced a depression-oriented dataset built with BERT and K-means from a previous one that included tweets labeled for sentiment analysis and DEENT-Generic and DEENT-Bert. The DEENT flavors achieved prominent accuracy, balanced accuracy, precision, recall, and f1-score regarding RF, SVM, XGBoost, RNN, and CNN, showing their ability to classify tweets as depressive or non-depressive. These results are due to DEENT being based on the encoder-only architecture, attention mechanism, and fine-tuning. Remarkably, thanks to its fine-tuning, DEENT-Bert performed better at detecting depressive and non-depressive tweets than MentalBERT. In turn, DEENT-Generic classified the depressive tweets better than MentalBERT, but the former was more effective at detecting depressive tweets than the latter; this is because MentalBERT had problems with the minority class related to imbalanced data. In this regard, using the balanced accuracy metric corroborated that DEENT is a robust model when using imbalanced data since the difference between accuracy and balanced accuracy was negligible. Overall, we concluded that DEENT is a promising solution to address depression detection using social network data.  
In future work, we plan to explain and improve DEENT using explainable artificial intelligence techniques, such as Local Interpretable Model-Agnostic Explanations and SHapley Additive exPlanations. The explanations given by these techniques can provide relevant information about the word's importance, which can be used to reduce the input data dimensionality of DEENT. We also intend to strengthen DEENT's performance by considering the information about relevant words and via hyperparameter tuning and text preprocessing optimization, reducing the noise in the input data to improve model learning. Furthermore, we aim to extend DEENT for multi-label classification centered on the most common mental illnesses (depression, anxiety, and suicide) and evaluate it with available demographic data.  
Conceptualization, R.N.B., O.M.C.R. and C.A.A.; methodology, R.N.B., O.M.C.R. and C.A.A.; software, R.N.B. and O.M.C.R.; validation, R.N.B., O.M.C.R. and C.A.A.; formal analysis, R.N.B., O.M.C.R. and C.A.A.; investigation, R.N.B., O.M.C.R. and C.A.A.; resources, R.N.B. and O.M.C.R.; data curation, R.N.B.; writing--original draft preparation, R.N.B.; writing--review and editing, R.N.B., O.M.C.R. and C.A.A.; visualization, R.N.B.; supervision, O.M.C.R.; project administration, O.M.C.R.; funding acquisition, O.M.C.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universidad del Cauca and the Foundation for the Future of Colombia--Colfuturo--through the Master's scholarship of the student [PERSON].  
Not applicable.  
The data are available on IEEDataPort.  
Conflicts of Interest: The authors declare no conflicts of interest. The financiers had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.