Hey @rasel277144, if I understand correctly you’d like to classify whether whether a user is “depressed” based on their posts?
In this case, you could concatenate all the user posts and treat it as a standard classification problem and Sylvain has created a nice tutorial for this task here.
Having said that, you will probably run into limitations with the maximum context size of models like BERT (typically just a few paragraphs), so you might want to see if models like BigBird or LongFormer can help as their context size is 8x that of BERT. If that’s still not sufficient, you might want to adapt some of the suggestions in this thread to text classification (e.g. you could create an embedding for each user post, average the embeddings, and then use those embeddings for a simple logistic regression classifier)
PS I put “depressed” in quotes because I assume this is not a phenomenon we can hope to capture accurately from written text alone. I also suggest treading very carefully in this domain as there’s plenty of public examples where using NLP to diagnose patient well-being leads to bad outcomes.