Text analysis identifies trends across a large number of text-based documents. While text analysis is frequently performed by software or a programming language, such as Python or R, there are several web-based tools that are entry points to this method. Key trends in text analysis include word frequency and changes in vocabulary over time.
This guide is a companion to the Text and Data Mining Sources infoguide and is meant to describe how to text mine using a variety of tools and methods. Each tool discussed is free to use. Each page details a different tool, includes resources such as documentation and tutorials, and a brief introduction on how to get started using that tool.
The first half of this guide can be found here, and it includes the following:
The Qualitative Research and Tools infoguide also discusses text analysis, specifically the section on using software, which is included in this guide. The software section describes how computers can help your analysis, and details how to use several licensed software programs, including NVivo, QDA Miner, Atlas.ti, and MAXQDA.
See the following related infoguides for further help with your work of digital scholarship or digital project.