Text Analysis Tools

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:

• Where to find sources to text mine, including text collections 
• Issues with attempting to text mine current news sources
• Social media data sources and tools for extracting social media data 

The ProQuest TDM Studio infoguide discusses how to access and use their platform, including the data visualization component of the product. 

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. 

• Part one of this guide: Text and Data Mining Sources 
• Software for text analysis: Qualitative Research and Tools
• ProQuest TDM Studio

• Find Data for Analysis. Use this guide to identify places to find data to use in digital scholarship projects. Learn about how to cite data, find datasets, find qualitative data, browse data, and how to access limited use datasets. 
• Research Data Management. Learn best practices for managing research data, including documenting and describing data and organizing and naming files. 
• Digital Humanities. Learn about the concepts, methodologies, and tools of digital humanities, and view various digital humanities projects.
• Data Visualization. Learn about data visualization, including getting started, planning, the different types, tips and tutorials, and visualization software and tools. 
• Learn Python. This guide will take you thorugh how to learn Python, how to install Python and its packages, tutorials and documentation, and much more. 
• Learn R. If you are interested in learning and applying R to your digital scholarship project, this guide will point you to useful tutorials, statistical analysis in R, packages to install and load for data management and graphics, and best practices when programming in R.