Skip to main content

University Library, University of Illinois at Urbana-Champaign

Text Mining Tools and Methods

This guide contains resources for researching with text mining

Text mining overview

What is text mining?

Text mining is a research practice that involves using computers to discover information in large amounts of unstructured text.

Unstructured text is data not formatted according to an encoding structure like HTML or XML.

Examples of unstructured data used for text mining include journal and news articles, blog posts, and email

Researchers use text mining tasks such as:

  • sentiment analysis
  • entity extraction
  • document summarization

By using these methods, researchers can make connections and draw conclusions about the content of large text corpora. 

The image on the right is one example of what you can do with text mining. This pie chart represents the total words spoken by characters in the Jacobean play The Revenger's Tragedy.

Credit: Chart by Pgogy, available via Creative Commons license.

 

 

Text mining goals

Why do text mining?

Text mining helps researchers detect patterns and connections in large volumes of textual material.

According to researcher Marti Hearst, "In text mining, the goal is to discover heretofore unknown information, something that no one yet knows and so could not have yet written down." Text mining enables researchers to draw conclusions from large volumes of material they would not be able to otherwise read, synthesize, and incorporate into their scholarship.

Researchers in fields ranging from biological sciences to the humanities have begun using text mining to detect patterns and discover unknown information. 

Questions? Ask us!

If you have questions about text mining, please contact:

Scholarly Commons
306 Main Library
217-244-1331

CITL Data Analytics

citl-data@illinois.edu