Methodica is a collection of research methods and techniques for analyzing text. Computation has produced new and exciting ways of studying text in the Digital Humanities, and many of these methods do not require the use of expensive programs or detailed programming knowledge. This site describes common or interesting sequences of actions, or recipes, showing users how to combine freely accessible resources to perform various analytic tasks.

Each recipe begins by listing required ingredients (text analysis tools, pieces of code, texts in certain formats, etc.), walks the user through the steps of a process, and concludes with links to further information and additional steps the researcher can try out. Recipes are tagged with the categories of text analysis they apply to, and with a difficulty level.

The site includes a number of special types of recipes. Tutorials use multiple recipes to show a higher-order research process; examples demonstrate a concrete process; utilities are single-purpose processes, often for refining data; and backgrounders offer information on text analysis more generally and notable pieces of software used for it. The site also includes a Glossary of text analysis terms that may be unfamiliar to researchers new to the field.

Methodica is a companion to the Text Analysis Portal for Research (TAPoR): while TAPoR helps researchers discover tools for conducting their research, Methodica helps them discover viable methods and practices. Recipes are linked with tools on TAPoR, allowing a researcher to jump between information on text analysis tools and practices.

The digital humanities community benefits from shared experience and learning how others make use of recipes. You can share your experience by adding your own recipes to Methodica’s collection. More information about recipe authoring is available on the Recipe Stylesheet page. We also have a Glossary that we hope you will add to.


The Methodi.ca team is made up of: Dr. Geoffrey Rockwell, Antony Owino, Gregory Whistance-Smith, Jason Bradshaw, Jinman Zhang, Kaitlyn Grant, and Kynan Ly.