Digital Approaches to Text Analysis: opening new paths for textual scholarship

The purpose of this course is to introduce students of literature, and more generally the humanities, to digital humanities methodologies for the study of text. Among the various digital approaches which will be introduced in class are concordances (retrieving occurrences of words), semantic similarity detection (finding similar passages across texts), sentiment analysis, stylometry (analysis of literary style), and topic modeling (automatic classification of texts). The course will highlight how these approaches to text can provide new avenues of research, such as tracing intellectual influence over the longue durée, or uncovering the distinguishing stylistic features of an author, work, or literary movement. Students need no prior knowledge of such methods, and the course will aim at providing the basics of computer programming in Python to give students the necessary tooling to conduct a digital humanities project. The source material for the course will be drawn from literary sources, and students will be free (and encouraged) to use texts which are relevant to their own research interests. Students will need to bring a laptop to class.