Text & Data Mining Sources

Use this guide to identify resources available for text and data mining. The guide is broken out into these main sections:

• Access Collections
  Lists library subscription and popular free resources which allow or are suitable for text and data mining.
• Historic Newspaper & Text Data
  Lists raw XML text data purchased by the University Libraries to be used for text and data mining.

The following sections explain each source's limitations and provides recommendations for available sources.

• Current News Sources
• Social Media Data

It is important to note that not all online resources allow text mining and that there are legal and ethical limitations to consider. Additionally, if you are going to use Artificial Intelligence (AI) tools with library-licensed materials, you need to contact us first because some vendors explicitly forbid doing so. Email datahelp@gmu.edu and we will help you.

This guide is a companion to the Text Analysis Tools InfoGuide, which describes how to text data mine using free, web-based tools.

Please see the following related infoguides for further help with your digital project.

• Qualitative Research and Tools
  text analysis can be performed using qualitative research software.
• Limited Access Data Sets 
  lists data sets that need to be used in the Data & Digital Scholarship Services Lab or have another restriction. To use these data sources, please contact us first. We have limited staff and we want to be sure that someone is available to help you. Use of these data sources are restricted to current George Mason University students, staff and faculty.
• Citing Data
  provides detailed information and examples on how you should cite data in your research.
• Data Visualization 
  guidelines and best practices for visualizing data with a wide range of tools.
• Find Data for Analysis: Data for Practice & Projects
  links to resources that include text corpora among their list of data sources and resources suitable for natural language processing and machine learning.
• Research Data Management Basics
  for understanding best practices for managing your data and keeping organized.
• Software for Digital Scholarship 
  see R, NVivo, and QDAMiner.