Description
This course is an introduction to the field of text analysis using digital tools. Whereas most of us know how to make a word cloud, do you know it is a lot more than a pretty picture. For example, what does word frequency tell us about important themes in a text? What is a text really about? How can we perform topic modeling on a text? How can we use digital methods to measure an author’s style? How do we use these tools to identify “anonymous” authors? Is the author angry or happy? How do you compare texts by the same author and different authors? What is “differential” reading?
Methods
Students will learn how to analyze computationally content, style, and metadata of literary and non-literary texts (not always in English) using readily available digital platforms most of which are web-based. Class will consist of lectures and labs.
Goals
Course level:
Students will learn how to identify, analyze, and discuss digital tools for text analysis. They will build their own final projects that will synthesize at least two approaches to text analysis and present them to a public audience.
Program level:
Students will meaningfully compare intellectual materials of different or opposing types: textual with material artifacts; narrative with non-narrative texts; artistic with analytical modes of thought. (6,8)
Appreciate the benefits, problems, and intellectual challenge of comparative study across historical, cultural, or generic boundaries. (5,6)
Demonstrate effective expository skills, both orally and in writing. (7,8)
DH minor level:
Students will learn to identify, use, and discuss the advantages and disadvantages of different DH methodologies and tools and be able identify and use key DH terms and concepts
Students will create DH projects using the tools covered in lab and be able to articulate and assess the success (or failure) of a DH project
Students will develop research questions that can be answered with DH tools and methodologies
Students will work collaboratively in groups to create projects that relate to their own research interests
Background image on this site is courtesy of Jiayu Huang and Zhengri Fan and was produced with Voyant 2.0 in HUMN 270, Fall 2015.