<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Mbrett on Journal of Digital Humanities</title><link>https://journalofdigitalhumanities.org/author/mbrett/</link><description>Recent content in Mbrett on Journal of Digital Humanities</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Sat, 01 Dec 2012 00:00:00 +0000</lastBuildDate><atom:link href="https://journalofdigitalhumanities.org/author/mbrett/index.xml" rel="self" type="application/rss+xml"/><item><title>Topic Modeling: A Basic Introduction</title><link>https://journalofdigitalhumanities.org/2-1/topic-modeling-a-basic-introduction-by-megan-r-brett/</link><pubDate>Sat, 01 Dec 2012 00:00:00 +0000</pubDate><guid>https://journalofdigitalhumanities.org/2-1/topic-modeling-a-basic-introduction-by-megan-r-brett/</guid><description>&lt;p&gt;The purpose of this post is to help explain some of the basic concepts of topic modeling, introduce some topic modeling tools, and point out some other posts on topic modeling. The intended audience is historians, but it will hopefully prove useful to the general reader.&lt;/p&gt;
&lt;h3 id="what-is-topic-modeling"&gt;What is Topic Modeling?&lt;/h3&gt;
&lt;p&gt;Topic modeling is a form of text mining, a way of identifying patterns in a corpus. You take your corpus and run it through a tool which groups words across the corpus into ‘topics’. Miriam Posner has &lt;a href="http://miriamposner.com/blog/?p=1335" title="Miriam Posner, 'Very basic strategies for interpreting results from the Topic Modeling Tool'"&gt;described topic modeling&lt;/a&gt; as “a method for finding and tracing clusters of words (called “topics” in shorthand) in large bodies of texts.”&lt;/p&gt;</description></item></channel></rss>