Topics in Methods
Computer-Assisted Textual Analysis
This course introduces students to the state of the art in the field of computer-assisted textual analysis. The course starts with an introduction to the Python programming language, and comprises both lectures and lab exercises. We cover a wide range of methods for textual analysis, from basic statistics to advanced notions in natural language processing. In particular, the course includes an introduction to machine learning and topic modeling (e.g. Latent Dirichlet Allocation). with illustrations based on real-world political corpora. By the end of this course, graduate students should be able to conduct in-depth analysis of textual data in their own research.
Format and Requirements
The course takes place in a computer lab. Familiarity with statistics (POL 2504 or equivalent) will be helpful to benefit from this course.
PLEASE NOTE: All courses with room numbers indicated are DUAL DELIVERY, EXCEPT for courses in SSH3130 unless otherwise indicated.
ONLINE-S = Online Synchronous
ONLINE-A =Online Asynchronous