Using spam classification in a different application?
I'm skeptical. The reason simple Bayesian filtering works for spam is that spam messages typically use a quite different vocabulary than legitimate messages.
Anecdotally, people who sell pharmaceuticals use the same words and phrases in their legitimate business correspondence as in some types of spam; so they get bad filtering results on pharma spam, while the spam filter quickly learns to correctly discard dating, Nigerian fraud, stock scams etc. (Pharma spam might still contain various hyperbolic phrases etc which set them apart even from non-spam marketing messaging, though.)
Business bullshit lingo tends to look the same whether the underlying plan is sound or not. You may be able to filter out the worst gibberish, but word-token level analysis is simply not a good indicator of whether actual sound thought went into composing those words into a particular arrangement.
I want to use the concept of spam classification and apply it to a business problem where we identify if a vision statement for a company is good or not. Here's a rough outline of what I've come up with for the project. Does this seem feasible?

Prepare dataset by collecting vision statements from top leading companies (i.e. Fortune 5000)
Let features = most frequent words (excluding non-alphanumerics, to, the, etc)
Create feature vector (dictionary) x of all words listed above
Use supervised learning algorithm (logistic regression) to train and test data
Let y = good vision statement and return the value 1; y = 0 if not good