Assignments

  1. Learn Things: You’re going to no-joke come to every class and participate every time. This means: you will always have read the text or played with the software before class, thought about it enough to have either one brilliant or three mediocre things to say, will actually say those things in class or at least think them very hard at other people, and will do your utmost best to learn something new. I will ask what you have learned, so be ready to say something.
  2. Data Set: You will need to create and clean a data set to study. This is like 85% of the work digital humanists do, because data tends to be messy! There is no right amount of data, but we will aim for at least 25 texts to start. Your data set will be accompanied by a ~750-word reflection on the decisions you made about sourcing, selecting, curating, and cleaning your corpus. (5 bonus points if you do this using gutenbergr in R.)
  3. Stylometry: Using the stylo package in R, generate a cluster analysis and a bootstrap consensus tree on a corpus of your creation. Your visualizations should be accompanied by a ~750-word reflection on the strengths and limitations of this approach towards your corpus, with a discussion of what changes you might make to improve your corpus, if any.
  4. Topic Modelling: Using the TMT, generate topic models on a corpus. Visualize these models using Excel or rawgraphs.io. Your visualizations should be accompanied by a ~750-word reflection on the strengths and limitations of topic modeling, particularly considering your own chosen corpus.
  5. PCA: Using Voyant, generate a simple PCA (in one or more of the three flavors). Your visualization(s) should be accompanied by a ~750-word reflection on what textual dimensions you think the components are capturing to account for the difference in your corpus.
  6. Contrastive Analysis: Using the stylo package in R, you will explore rolling deltas, zeta, and the oppose() function, creating a series of contrastive analysis visualizations that demonstrate knowledge about authorial similarity and difference. Your visualizations should be accompanied by a ~750-word reflection on the strengths and limitations of these methods, particularly considering your own chosen corpus.
  7. Network Analysis: Using your data set or exploring a related one, you will use Palladio to create network visualizations that illuminate an otherwise hidden aspect of your data set. Your visualizations should be accompanied by a ~750-word reflection on the strengths and limitations of this method.
  8. Syllabi Project: Apply what you’ve learned this semester to a corpus of English syllabi. Generate some new knowledge or insight by using digital methods to analyze the department’s syllabi.
  9. Final Exam: This final exam will cover core terms and concepts from the course and will include longer essay questions.