An introduction to text mining/analysis and resources for finding text data, preparing text data for analysis, methods and tools for analyzing text data, and further readings regarding text mining and its various methods.

Text analysis is the process of examining texts to discover hidden information, gain new insights, or answer specific research questions using algorithms that quickly identify facts, patterns, and relationships in a body of texts. This information can be converted into structured forms for visualization using charts, graphs, mind maps, word clouds, and more.

This workshop will introduce text analysis and visualization with Voyant, an open-source, web-based application. Participants will learn how to use multiple tools within Voyant and their benefits/limitations, some best practices for text visualization, and some ways text analysis could be incorporated into their research.

Topic modeling is a text mining method that uses algorithms to discover hidden patterns and structures in large collections of texts. A topic modeling program extracts abstract “topics” by identifying and grouping words that frequently co-occur in a text. This workshop will introduce topic modeling with MALLET, a Java-based package for statistical natural language processing, topic modeling, and other machine learning applications to text. Participants will learn how to create and interpret topic models using MALLET.

This LibGuide introduces users to optical character recognition (OCR), outlines OCR best practices, provides information and resources for OCR tools, and links to example OCR projects.