Well if you're willing to relocate to the Bay Area I could set you up in an office with a variety of tools to analyze and classify text and do all sorts of analysis on it, I'd even pay you :-) (yes I've got a couple of job openings where this pretty much describes the job)
That said, converting large documents into data sets is examined in a number of papers, you may find yourself getting more traction by splitting the problem up that way, step 1) pull apart the document into a more useful form, then step 2) do analysis on those parts. They are interelated of course and some forms of documents lend themselves to disassembly better than others (scientific papers for example, easy to unpack, random blog posts, less so.
As for "where" to do it, the ideal place is a NoSQL cluster. This is what we've done at Blekko for years (and continue to do post acquisition) which is put the documents we crawl from the Internet into a giant NoSQL data base and then run jobs that execute in parallel across all of those servers to analyze those documents (traditionally to build a search index, but other modalities are interesting too.
That said, converting large documents into data sets is examined in a number of papers, you may find yourself getting more traction by splitting the problem up that way, step 1) pull apart the document into a more useful form, then step 2) do analysis on those parts. They are interelated of course and some forms of documents lend themselves to disassembly better than others (scientific papers for example, easy to unpack, random blog posts, less so.
As for "where" to do it, the ideal place is a NoSQL cluster. This is what we've done at Blekko for years (and continue to do post acquisition) which is put the documents we crawl from the Internet into a giant NoSQL data base and then run jobs that execute in parallel across all of those servers to analyze those documents (traditionally to build a search index, but other modalities are interesting too.