I have recently adjusted the syllabus of my course on digital humanities and text analysis and wanted to share some of the methods, tools and resources that I currently use. For some great tutorials and exercises on computer-based text analysis, you might want to check out the following:

• Hermeneutica – Computer-Assisted Interpretation in the Humanities
• The Programming Historian
• Introduction to Text Analysis: A Coursebook

Preliminary remarks

The target audience for the course are students in the media informatics Master’s program, i.e. students have a background in programming and data modelling. The course is divided in 3 phases: During phases 1+2, students spend 2×2 hours per week in class and learn the fundamentals of digital humanities and text analysis. Phase 3 is a free project phase, where groups of students work on their individual digital humanities projects and regularly meet with the lecturer to discuss the progress of their project.

Phase 1: Introduction to Digital Humanities

Week 1a

• General introduction to the course
• Introducing the DSH reading challenge: throughout the course, each student will present a preselected paper from the Digital Scholarship in the Humanities journal by summarizing its basic research goals, methods and results in 5 minutes (+5 minutes of general discussion)

Reading assignment: Prensky, M. (2001). Digital Natives, Digital Immigrants Part I. On the Horizon, 9(6), 1–6.

Week 1b

• Discussing the „digital“ and its implications, as in digital revolution, digital society, digital culture, digital natives, …

Reading assignment: Snow, C. P. (1959). The Two Cultures. London: Cambridge University Press.

Week 2a

• Discussing „humanities“
• What is special / challenging about digital humanities? Can you adopt the idea of digital natives and digital immigrants to scholarly disciplines?

Reading assignment: Michel, J.-B., Shen, Y. K., Aiden, A. P., Veres, A., Gray, M. K., Pickett, J. P., … Aiden, E. L. (2011). Quantitative analysis of culture using millions of digitized books. Science (New York, N.Y.), 331(6014), 176–82.

Week2b

• From Busa to Culturomics – A short history of the digital humanities
• Exercise: Building your own Index Thomisticus in 5 minutes (using Open Library and Voyant Tools)
• Exercise: Discussion of the Google Ngram Viewer (representativeness, OCR errors, etc.)

Reading assignment: Svensson, P. (2010). The Landscape of Digital Humanities. Digital Humanities Quarterly, 4(1), 1–31.

Week3a

• Defining the digital humanities: Overview and discussion of existing definitions
• Working definition for the course:
  (1) DH as use of digital tools / methods / resources in the humanities, and
  (2) DH as humanities, investigating digital culture.

Week3b

• Basic introduction to literary studies
• Overview of literary theories and typical research questions in that field
• Exercise: Analyzing “Every breath you take” in class

Reading assignment: Moretti, F. (2000). Conjectures on world literature. New Left Review, (Jan / Feb), 54–68.

Phase 2: How to do digital humanities? A hands-on introduction to computer-based text analysis.

Week4a

• Introduction to (close and) distant reading
• Examples for the application of distant reading approaches
• Tools for distant reading (Voyant, To See or Not to See)

Week4b

• Data acquisition – How to get digital texts?
• Overview of text repositories: Open Library, Project Gutenberg, Deutsches Textarchiv, TextGrid Repository, Folger Digital Texts, opensubtitles.com
• Tools: wget, beautiful soup

Week5a

• Introduction to data cleaning with the bash command line and regular expressions
• Tools: UnixShell, CygWin

Week5b

• Beyond raw text – annotating with XML / TEI
• Utilizing document markup with XSLT

Week6a

• Introduction to natural language processing and automatic tagging
• Tools: WebLicht, TreeTagger, NLTK
  

Week6b

• Analyzing text: frequencies, concordances and collocations
• Tools: Voyant, AntConc

Week7a

• How to interpret frequencies ­– Introduction to statistics
• Tool: R studio

Week7b

• Introducion to stylometry
• Tool: R Stylo

Week8a

• Introduction to topic modeling
• Tool: MALLET

Week8b

• Distant reading the DSH: throughout the course, students have “close read” and discussed typical articles from the DSH journal – at the end of the course, we apply frequency lists and topic modeling to a larger corpus of DSH articles.

Phase 3: DH projects

Students work on their own research projects and apply text analysis techniques

Example topics:

• Stylistic change in the different editions of the fairytales by brothers Grimm’s
• Intertextuality: Shakespeare in literature / film / lyrics
• Quantitative analysis of drama according to speakers, structure, speech, etc.
• etc.