LibGuides: Digital Humanities Tools and Resources: Text Analysis & Data Mining

Introduction

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.

Tools

Voyant
Voyant is a web-based, dashboard-style tool that allows users to upload a corpora and visualize patterns in various ways. For instance, users can experiment with colorful word clusters that represent word frequency and visualize how specific words and phrases appear across texts in line graphs.
Mallet
Mallet is a machine learning software program that is used through the command line with Python. Though it requires some technical skill to install and run, it can produce powerful results by generating “topics,” or lists of words that frequently appear together in corpora.
Natural Language Toolkit
Natural language toolkit is a suite of Python tools that allows users to analyze human language data using classification, tokenizing, tagging, and more. Though it requires some technical expertise to run, it can be very useful for both teaching and analysis.
Gephi
Gephi is a visualization tool that allows users to make colorful graphs and networks from textual data by revealing links between textual objects, social network patterns, and more. Gephi is easy to use and popular among humanists and social scientists alike.
Beautiful Soup
Beautiful Soup is a web-scraping tool that uses python to pull data from HTML and XML files. It allows you navigate and search a document using a parse tree.
TAPoR Portal
TAPoR is not a digital humanities tool, but a portal to find curated lists of text analysis tools commonly used in the field. Examples of lists include easy to use tools and developing tools that offer exciting new possibilities.
R
R is a programming language commonly used by humanists to do statistical analysis and create visualizations.
Python
Python is a high level programming language commonly used in text analysis tools in the digital humanities.

Readings

Argamon, S., & Olsen, M. (2009). Words, patterns, and documents: Experiments in machine learning and text analysis. Digital Humanities Quarterly, 3(1). Retrieved from http://digitalhumanities.org:8081/dhq/vol/3/2/000041/000041.html

Brett, M. (2012). Topic modeling: A basic introduction. Journal of the Digital Humanities, 2(1). Retrieved from http://journalofdigitalhumanities.org/2-1/topic-modeling-a-basic-introduction-by-megan-r-brett/

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. https://doi.org/10.1108/AJIM-11-2021-0325

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. https://doi.org/10.1007/s13222-022-00414-1

Elliot, T., & Gillies, S. (2009). Digital geography and classics. Digital Humanities Quarterly, (3)1. Retrieved from http://www.digitalhumanities.org/dhq/vol/3/1/000031/000031.html

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.