Introducing myself to MALLET - Emerging Tech in Libraries
Here’s how I understand it: topic modeling, like other text mining techniques, considers text as a ‘bag of words’ that is more or less organized. It draws out clusters of words (topics) that appear to be related because they statistically occur near each other. We’ve all been subjected to wordles — this is like DIY wordles that can get very specific and can seem to approach semantic understanding with statistics alone.
One tool that DH folks mention often is MALLET, the MAchine Learning for LanguagE Toolkit, open-source software developed at UMass Amherst starting in 2002. I was pleased to see that it not only models topics, but does the things I’d wanted Oracle Data Miner to do, too — classify with decision trees, Naïve Bayes, and more. There are many tutorials and papers written on/about MALLET, but the one I picked was Getting Started with Topic Modeling and MALLET from The Programming Historian 2, a project out of CHNM. The tutorial is very easy to follow and approaches the subject with a DH-y literariness.
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