So in my head, I have an idea about what this architecture should look like, or at least behave, but I am having trouble implementing it. So let me describe the problem, and if anyone has an idea on how to actually implement it let me know. Or if I am over-thinking a solution.
I am trying to classify accounts into one of two groups, good and bad. I have multiple text documents per account. What I want to do is take the text documents for a single account and order them chronologically. Then use a recurrent neural network to essentially learn an embedding for each document (traditional text recurrent architecture should work fine), and then plug those embeddings into a document-level recurrent neural net, to predict when an account switches from good to bad. That way, as new documents come in per account I get updated "badness" scores for an account, and potentially flag an account above some threshold. I would like some sort of end-to-end architecture too. Like I said, not sure how to get what is my head into the computer.
Here's my question, is anyone aware of an implementation like that?