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dc.contributor.authorGraham, Shawn
dc.contributor.authorWeingart, Scott
dc.contributor.authorMilligan, Ian 18:00:03 (GMT) 18:00:03 (GMT)
dc.descriptionThis article Published by the Editorial Board of the Programming Historian is made available under a Creative Commons Attribution 2.0 Generic License. Available at:
dc.description.abstractIn this lesson you will first learn what topic modeling is and why you might want to employ it in your research. You will then learn how to install and work with the MALLET natural language processing toolkit to do so. MALLET involves modifying an environment variable (essentially, setting up a short-cut so that your computer always knows where to find the MALLET program) and working with the command line (ie, by typing in commands manually, rather than clicking on icons or menus). We will run the topic modeller on some example files, and look at the kinds of outputs that MALLET installed. This will give us a good idea of how it can be used on a corpus of texts to identify topics found in the documents without reading them individually.en
dc.publisherThe Editorial Board of the Programming Historianen
dc.rightsAttribution 2.0 Generic*
dc.subjectTopic modelingen
dc.subjectNatural language processingen
dc.subjectDistant readingen
dc.titleGetting Started with Topic Modeling and MALLETen
dc.typeTechnical Reporten
dcterms.bibliographicCitationShawn Graham, Scott Weingart, and Ian Milligan. “Getting Started with Topic Modeling and MALLET.” The Programming Historian, September 2012.en
uws.contributor.affiliation1Faculty of Artsen

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