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I’m changing several things about my data, so I’m going to describe my system again in case anyone is interested, and so I have a page to link to in the future.
Everything is done using MySQL, Perl, and R. These are all general computing tools, not the specific digital humanities or text processing ones that various people have contributed over the years. That’s mostly because the number and size of files I’m dealing with are so large that I don’t trust an existing program to handle them, and because the existing packages don’t necessarily have implementations for the patterns of change over time I want as a historian. I feel bad about not using existing tools, because the collaboration and exchange of tools is one of the major selling points of the digital humanities right now, and something like Voyeur or MONK has a lot of features I wouldn’t necessarily think to implement on my own. Maybe I’ll find some way to get on board with all that later. First, a quick note on the programs:
Jamie’s been asking for some thoughts on what it takes to do this–statistics backgrounds, etc. I should say that I’m doing this, for the most part, the hard way, because 1) My database is too large to start out using most tools I know of, including I think the R text-mining package, and 2) I want to understand how it works better. I don’t think I’m going to do the software review thing here, but there are what look like a _lot _of promising leads at an American Studies blog.
A collection as large as the Internet Archive’s OCR database means I have to think through what I want well in advance of doing it. I’m only using a small subset of their 900,000 Google-scanned books, but that’s still 16 gigabytes–it takes a couple hours just to get my baseline count of the 200,000 most common words. I could probably improve a lot of my search time through some more sophisticated database management, but I’ll still have to figure out what sort of relations are worth looking for. So what are some?