8. Conclusion
Depression is a mental condition that can result in more serious
problems or even suicide if it is not properly and swiftly treated.
Since a complete history of postings might provide crucial information
to assist in better diagnosing patients, this type of research may be
advantageous to psychiatrists. The TF-IDF characteristics and other
characteristics that the authors believed might aid in a better
diagnosis of depression were used to test the logistic regression
model’s base model. Only the response time between posts improved the
fundamental model on particular data subsets among the features the
authors used, which also included post sentiment, post count, post
length, and average time between posts. In comparison to the model
described by Losada et al. [2], the authors’ F1 score using that
model was significantly higher. As can be seen in section 5, the main
goal was to identify words or groups of words that aid in the diagnosis
of depression. Using deep learning models, incorporating further
features not included in this paper, like the user’s gender, or
experimenting with different word embeddings could all improve the work.