I did this at my last job. ComplementNB trained on ~30,000 examples (which were freeform text, and very unbalanced across categories) in under a minute on a laptop CPU, and with log-weighting of scores, was about 85% accurate. I don't remember how fast it was at evaluating the tickets beyond 'basically instant', as it was a proof-of-concept that got cancelled by changing ticketing system.

Heard about classifiers a long time back. Will current ones resolve "reallyyyyyy beast mode" as a positive phrase like an LLM?

I was going to say, and I say this a little bit too often, a BERT model can do this even faster, with even less memory...