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The Information Systems and Statistics Research Seminar Series
Presented by the Paul H. Chook Department of Information Systems and Statistics
Scaling Text with the Class Affinity Model
Patrick Perry, Assistant Professor, NYU Stern School of Business
Thursday, March 29, 2018 @ 12:30pm-1:45pm
NVC 11-217, ISS Conference Room
From: Prof. Rongning Wu, Paul H. Chook Department of Information Systems and Statistics
Probabilistic methods for classifying text form a rich tradition in machine learning and natural language processing. For many important problems, however, class prediction is uninteresting because the class is known, and instead the focus shifts to estimating latent quantities related to the text, such as affect or ideology. We focus on one such problem of interest, estimating the ideological positions of 55 Irish legislators in the 1991 Dáil confidence vote. To solve the Dáil scaling problem and others like it, we develop a text modeling framework that allows actors to take latent positions on a "gray" spectrum between "black" and "white" polar opposites. We are able to validate results from this model by measuring the influences exhibited by individual words, and we are able to quantify the uncertainty in the scaling estimates by using a sentence-level block bootstrap. Applying our method to the Dáil debate, we are able to scale the legislators between extreme pro-government and pro-opposition in a way that reveals nuances in their speeches not captured by their votes or party affiliations.
Paul H. Chook Department of Information Systems and Statistics