Abstract
This chapter describes the basic definition of practical text mining, explains why text mining is important to the modern enterprise, and gives examples of text mining used in enterprise. It also discusses the challenges facing text mining, and provides an example workflow for processing natural language in analytical contexts and a simple text mining example. Text mining fits within many disciplines. These include private and academic uses. For academics, text mining may aid in the analytical understanding of qualitatively collected transcripts or the study of language and sociology. For the private enterprise, text mining skills are often contained in a data science team. To get started in text mining people need a few tools. They should have access to a laptop or workstation with at least 4GB of RAM. RAM is important because R's processing is done "in memory". Lastly, the computer needs to have an installation of R and R Studio.
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