I wanted to see if "super-steel" marketing actually matched reality, so I scraped ~500 threads from r/chefknives and ran sentiment analysis on the specific steels mentioned.

The results:

MagnaCut: 28:1 positive ratio. The hype is real.

Ginsan: The sleeper favorite. High satisfaction because it almost never chips.

VG-10: Most controversial. High volume, but highest statistical ratio of "micro-chipping" complaints.

How I saved tokens (Inverse Masking): Feeding raw threads into an LLM just to find common terms like "Wüsthof" is a waste of money. I built a hybrid pipeline instead:

Fuzzy Match: Fuse.js catches 80% of common brands for $0.

Mask: Replace those entities with placeholders.

LLM: Feed the "masked" text to the model to catch obscure artisan makers and nuanced sentiment the fuzzy matcher missed.

Stack is Node.js + MongoDB. Full charts and the steel-by-steel breakdown are here: https://new.knife.day/blog/knife-steel-comparisons/all

Would love to hear thoughts on the methodology or if the data misses your favorite steel.