A simplistic linear and multiprocessed approach to sentiment analysis using Gzip Normalized Compression Distances with k nearest neighbors
Original work that this concept is based on: https://aclanthology.org/2023.findings-acl.426.pdf Paper authors also have implementation code here: https://github.com/bazingagin/npc_gzip
This is not a fork of their work, this one is written myself based on what I read in the paper just to see if I actually understood what was going on. They achieve a higher accuracy than I found personally on a separate dataset, but it would appear there's something interesting and useful about this methodology.
I wonder about further "feature extraction" based on this sort of "compression lengths" as features. For example, rather than NCDs, maybe instead the compression ratio from original string to compressed sizeadd would be even more useful than NCDs, since (I believe) the reason this works is statistical similarities in words/phrases and their syntactic uses which Gzip uses for compression.
IDK. this just shouldnt work at all IMO :D