I'm curious, what is the use case for open-ended labeling like this? I can think of clustering ie finding similar tweets but that can also just be done via vector similarity. Otherwise maybe the labels contain interesting semantics but 6000 sounds like too many to analyze by hand. Maybe you are using LLMs to do further clustering and working on a graph or hierarchical "ontology" of tweets?