Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 4-Day Remote Seminar Taught by Brandon Stewart, Ph.D., Justin Grimmer, Ph.D., and Molly Roberts, Ph.D.

We live in an era of data abundance: never before has so much been so easy to acquire. Social scientists and industry practitioners alike are left with a new problem: how to analyze the now readily accessible mountains of information. A burgeoning array of algorithms, statistical methods, and research designs make analysis of this information possible. These new forms of data and new statistical techniques provide opportunities to observe behavior that was previously unobservable, to measure quantities of interest that were previously unmeasurable, and to test hypotheses that were previously impossible to test.

This seminar will overview the field of “Text as Data” with an emphasis on making inferences with social data. The course is organized around the tasks in the research process: discovery, measurement, and inference. We will introduce methods from natural language processing and machine learning (such as clustering, topic modeling, supervised classification, etc.) while demonstrating through applications how they can be incorporated to learn new facts about the social world. Our approach balances teaching you tangible skills now with helping you to see the general problems and how to apply the numerous new (and still being developed) tools to text problems. We will introduce specific examples with R code that will enable you to apply tools to your own problems after the course is over. In each case, we will provide a framework so you know what new tools are trying to accomplish and how you can use them in your work.