@article {brett_topic_2012,
	title = {Topic Modeling: A Basic Introduction},
	journal = {Journal of Digital Humanities},
	volume = {2},
	number = {1},
	year = {2012},
	note = {00000},
	abstract = {Brett{\textquoteright}s article purposes to introduce and exemplify topic modelling tools. Categorized as a form of text mining, Brett points to topic modelling as a way of sourcing out patterns in a corpus. In order to describe how topic modelling works, Brett illustrates using this analogy: "imagine working through an article with a set of highlighters. As you read through the article, you use a different color for the key words of themes within the paper as you come across them. When you were done, you could copy out the words as grouped by the color you assigned them. That list of words is a topic, and each color represents a different topic." Brett lists the "ingredients" necessary to successfully use topic modelling: a large corpus, familiarity with that corpus, a tool designed for topic modelling, and the knowledge to understand your results. While not necessarily useful as evidence, Brett argues that topic modelling is a great discovery tool.  },
	keywords = {Beginnings, No. 1 Winter 2012, Vol. 2},
	url = {http://journalofdigitalhumanities.org/2-1/topic-modeling-a-basic-introduction-by-megan-r-brett/},
	author = {Brett, Megan R.}
}