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 1 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 2 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 1 where using NLP to diagnose patient well-being leads to bad outcomes.