Creating Topic Models with JSTOR’s Data for Research (DfR)

Here are some instructions for creating the same types of topic models of JSTOR’s journals that I did with Critical Inquiry and Signs.

These instructions are designed for someone using a Mac or Linux platform. (The differences below between using Linux and a Mac should be apparent to anyone who uses Linux, so I’m not going to indicate them here; it’s mainly where files are stored.) All of this should work on Windows, but you’ll need to install Cygwin or use alternate shell commands. MALLET has slightly different installation instructions for the Windows platform as well, I believe.

  1. Download and install MALLET.
    • Download the file (I assume it will be in /Users/yourusername/Downloads)
    • open Terminal/shell (this is in the Applications/Utilities folder on a Mac)
    • cd to the directory where you downloaded it (something like:
      cd /Users/yourusername/Downloads
      if you use Downloads as default directory.)
    • Now enter these commands:
      tar -xzvf mallet-2.0.7.tar.gz
      cd mallet-2.0.7
  2. 2. Download and unzip DfR data.
    • Create a DfR account. Log in.
    • Find the journal you want. Make sure the total number of issues is less than 1000 or be content with a random sample. (You can request a higher limit with an explanation of why you need it, or you can download multiple files.)
    • Go to the “Submit New Request” tab.
    • Select Citations Only AND Word Counts.
    • Select CSV for Output Format.
    • Give a job title.
    • Click “Submit Job”
    • Wait for notification email.
    • When you get it, go to “My Requests” page,
    • Use your browser’s “Save As” feature to download the “Full Dataset” file to the MALLET directory (I’m assuming it’s /Users/yourusername/Downloads/mallet-2.0.7).
    • Go to terminal. Type
      $unzip 2012..[bunch of numbers]..zip
  3. 3. Pre-processing the JSTOR data.
    • Download Andrew Goldstone’s count2txt. Save it in the same directory you’ve been using.
    • Type
      perl -v
    • Unless the output of that command says “perl 5, version 14,” open count2txt in a text editor.
    • Find the line of the code that reads use v5.14;.
    • Delete the line, or add a #before it. (You could also change “14″ to the version of perl that you use.)
    • Enter
      perl count2txt --multifile wordcounts/*.CSV
    • Create a txt-files only directory for MALLET to work on:
      mkdir text
      cp wordcounts/*.txt text
  4. 4. Run Mallet
    • Enter (note that this—and all other commands here—should all be on the same line):
      bin/mallet import-dir --input text --output topic-input.mallet --keep-sequence --remove-stopwords
    • Now run the topic-modeler:
      bin/mallet train-topics --input topic-input.mallet --num-topics 10 --output-topic-keys jstor.model.txt
    • Now look at file jstor.model.txt for your results.

    You probably will want to add more topics than 10, but this shouldn’t take too long for a first experiment. MALLET also has a lot of parameters you can experiment with. You’ll probably want to add your own stop words. In the same directory, you can create a list with your own stop words in a text editor and save it as “stop.txt”. Then, try this command to re-create the MALLET input file:
    bin/mallet import-dir --input text --output topic-input.mallet --keep-sequence --remove-stopwords --extra-stopwords stop.txt

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