There are numerous tools for text and data mining available for use, both web based and as downloadable software. Provided here is a selection of tools that may be useful for different levels of coding or programming knowledge and skills.
Free web based program supporting a variety of programming languages, including Python and R, to be used for both text and data analysis. An interactive workspace in which a single document (notebook) can be used to run code and visualise output. Quinn Dombrowski's list of digital humanities courses and tutorials in the form of Jupyter notebooks might be useful. GLAM Workbench also has a variety of resources in the form of Jupyter notebooks for working with data from Australian galleries, libraries, archives, and museums.
Open source software for data analysis and visualisation. Python knowledge is required, however a graphical user interface is provided so no programming knowledge is necessary.
Stylometric tools for text analysis, intended for use with R software. A basic understanding of R functions is required. No programming is required as a GUI is provided, however programming is an option for more advanced R users.
Web based text analysis tool. Use primary sources available via the University library, or upload your own content. Further information about accessing and using Gale Digital Scholar Lab is available here through the University library's Digital Humanities services.
Software intended for the analysis of text and qualitative data. Access to NVivo is available to University of Adelaide staff and students, to be used on one personal device only. Learn more here.
Web based text analysis tool. Create a corpus to analyse, by pasting plain text or URLs, opening an existing corpus, or directly uploading files. Further information about accessing and using Voyant Tools is available here through the University library's Digital Humanities services.
This Guide explores the different types of data available from Trove – what it is, how you can access it, and what you can do with it. It aims to give researchers a critical understanding of Trove data, both its limits and its possibilities.
