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Digital Humanities Tools and Resources

Use this guide to learn about the field of Digital Humanities, software tools for humanist research, and resources to get started on new projects.

Text Analysis & Data Mining


Computational text analysis tools allow scholars to read bodies of text in new ways by using machine learning to pick up on word frequency patterns in texts. This process, often called “distant reading” or “topic modeling,” can complement traditional close reading by providing insights into patterns such as word usage, psychological tendencies, and language commonly associated with historical events, etc. that a human might not notice without the assistance of computational tools. To explore the capabilities of text analysis programs, please refer to the list of commonly used tools below. 



Argamon, S., & Olsen, M. (2009). Words, patterns, and documents: Experiments in machine learning and text analysis. Digital Humanities Quarterly, 3(1). Retrieved from

Brett, M. (2012). Topic modeling: A basic introduction. Journal of the Digital Humanities, 2(1). Retrieved from

Chen, Ho, S.-Y., & Chang, C. (2023). A hierarchical topic analysis tool to facilitate digital humanities research. Aslib Journal of Information  Management, 75(1), 1–19. 

El-Hjj, Zamani, M., B Büttner., Martinetz, J., Eberle, O., Shlomi, N., Siebold, A., Montavon, G., Müller,    K.-R., Kantz, H., & Valleriani, M. (2022). An Ever-Expanding Humanities Knowledge Graph: The Sphaera Corpus at the Intersection of Humanities, Data Management, and Machine Learning. Datenbank-Spektrum : Zeitschrift Für Datenbanktechnologie : Organ Der Fachgruppe Datenbanken Der Gesellschaft Für Informatik e.V, 22(2), 153–162.

Elliot, T., & Gillies, S. (2009). Digital geography and classics. Digital Humanities Quarterly, (3)1. Retrieved from 

Gregory, I., Donaldson, C., Murrieta-Flores, P., & Rayson, P. (2015). Geoparsing, GIS, and textual analysis: Current developments in spatial humanities research. International Journal Of Humanities & Arts Computing: A Journal Of Digital Humanities, 9(1), 1-14.

Sharma, Kumar, S., & Sharma, A. (2021). Literature and Cultural Studies Through Data Mining. ICFAI Journal of English Studies, 16(4), 119–125.


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