What you are proposing is a statistically generated version of the Gunning Fog Index (http://en.wikipedia.org/wiki/Gunning_fog_index) or the Flesch–Kincaid test (http://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readabil...).
If I were Google, I'd try that, but I'd also try something like working out percentage deviation from a Markov chain generated from their crawl. A method like that would show that my first sentence is pretty unreadable, while an algorithm based on word complexity would see it as pretty simple.
Holy Shnikes, that's a tough one to parse! I wasn't sure what you were saying here, so I'm gonna break it down, working from the end of the sentence:
1. I'd be lying if I said I don't doubt you are not incorrect
2. I'd be lying if I said I don't doubt you are CORRECT
3. I'd be lying if I said I don't think you are INCORRECT
4. I'd be lying if I said I think you are CORRECT
5. I think you are INCORRECT
The idea is that each of the previous statements are saying basically the same thing; I'm just cancelling negatives each time. Anyway, am I correct to assume that you think that the GP is incorrect?
I'd be lying if I said I don't doubt you are correct
I'd be lying if I said I think you are correct
Yes, I think the GP is incorrect.
Indeed, that was my second thought, but I wonder if the gains are really all that large over a raw statistical analysis of the word bag, and whether they're worth the extra analysis space/time. It really depends on what Google is planning on doing with this metadata, internally; if an order-of-ten precision is fine (to pick out decisive categorizations), the raw analysis may be all that's needed.