Riga Technical University Riga, Latvia
Module tutors:
Christopher Ohge (Institute of English Studies, University of London) [CO]
Martin Steer (School of Advanced Study, University of London) [MS]
Summary: this course will serve as both a general introduction to working with texts in digital humanities as well as the R programming language.
Outcomes: by the end of the module, students will be able to
Understand how digital humanities practitioners work with and manage texts
Understand the ways that computation can aid critical interpretation
Understand the meaning of a corpus, and why it is useful
Understand the basic syntax of the R programming language
Ability to perform basic text analyses with R
Required Software
AntConc (https://www.laurenceanthony.net/software/antconc/).
R (https://cran.r-project.org/mirrors.html).
RStudio Desktop (https://www.rstudio.com/products/rstudio/download/).
Monday 16 Sep
Lecture 1: Welcome; What is Digital Humanities? [CO and MS]
Lecture 2: Data Management and working with Texts in Digital Humanities [MS]
Tuesday 17 Sep
Lecture 3: What is Text, What is Text Analysis, and What is Distant Reading? [MS and CO]
Lecture 4: Computer-assisted interpretation: Hathi Trust bookworm exercise. [MS]
Wednesday 18 Sep
Lecture 5: Voyant tools [CO]
Lecture 6: Intro to corpus linguistics and analysis with AntConc [CO]
Thursday 19 Sep
Lecture 7: Intro to R, part 1 [CO]. Access the R notebook. See also the html file of the notebook. NOTE: right click on the links and Save Link As, then you will be able to open it in your browser.
Lecture 8: Regular Expressions [MS]. Access the regular expressions slides. See also the regex cheat sheet.
Intro to R, part 2 [CO]. Access the R notebook. See also the html file of the R notebook.
***
Monday 23 Sep
Review R syntax and conditionals [MS].
Access the R notebook on conditionals.
Review Intro to R, part 2 [CO].
Tuesday 24 Sep
Finish reviewing Intro to R, part 2; Lexical variety stats and visualisations. [CO]
Lecture 9: Stylo package in R for stylometry (distance measurements, Craig’s Zeta, network graph). [CO]
Wednesday 25 Sep
Lecture 10: [CO] Lexical dispersion plot; Put POS tags into use. [CO]
Lecture 11: Tidy text in R: texts into dataframes, gutenbergr package and corpus comparison. [CO]
Thursday 26 Sep
Lecture 12: Tidy text in R: sentiment analysis [CO].
A critique of sentiment analysis [MS]; Course review.
Suggested Readings
Eve, Martin. Close Reading with Computers: Textual Scholarship, Computational Formalism, and David Mitchell’s Cloud Atlas (Stanford UP, 2019).
Gries, Stefan. Quantitative Corpus Linguistics with R, 2nd edition (Routledge, 2017).
Jockers, Matthew. Text Analysis with R for Students of Literature (Springer, 2014).
––. Macroanalysis (U of Illinois P, 2013).
Moretti, Franco. Graphs, Maps, Trees (Verso, 2007).
Piper, Andrew. Enumerations (U of Chicago P, 2019).
Rockwell, Geoffrey and Stefan Sinclair. Hermeneutica: Computer-Assisted Interpretation in the Humanities (MIT P, 2016).
Silge, Julia, and David Robinson. Text Mining with R: A Tidy Approach (O’Reilly, 2017).
Underwood, Ted. Distant Horizons (U of Chicago P, 2019).