Why wouldn't you use OP's approach to build up the representative embeddings, and then train the MLP on that?

That way you can effectively handle open sets and train a more accurate MLP model.

With your approach I don't think you can get a representative list of N tweets which covers all possible categories. Even if you did, the LLM would be subject to context rot and token limits.