Keyword Assisted Topic ModelsThe proposed methodology is implemented via an open-source software package keyATM, which is available at https://cran.r-project.org/package=keyATM. We thank Soubhik Barari, Matthew Blackwell, Max Goplerud, Andy Halterman, Masataka Harada, Hiroto Katsumata, Gary King, Dean Knox, Shiro Kuriwaki, Will Lowe, Luke Miratrix, Hirofumi Miwa, Daichi Mochihashi, Santiago Olivella, Yon Soo Park, Reed Rasband, Hunter Rendleman, Sarah Mohamed, Yuki Shiraito, Tyler Simko, and Diana Stanescu, as well as seminar participants at the Institute for Quantitative Social Science Applied Statistics Workshop and the Japanese Society for Quantitative Political Science 2020 Winter Meeting for helpful discussions and comments on this project. Soichiro Yamauchi kindly gave us advice on methodological and computational issues.
Table 1: Frequency of each topic and its most frequent keywords. The table presents the label of each topic from the Comparative Agendas Project codebook, the number and proportion of the bills classified by the human coders of the Congressional Bills Project for each topic, and three most frequent keywords associated with each topic. Note that the Culture topic only has two keywords and the same keywords may appear for different topics.
['Topic label', 'Count', 'Percentage (%)', 'Most frequent keywords']
On average, there are 20 keywords per topic. The minimum number of keywords is 2 (Culture) and the maximum is 25 (Domestic commerce, Law & crime, Health, Education, Energy, Technology, Labor, and Civil rights). Each keyword represents a topic in meaningful ways. For example, “employee” and “benefit” for the Labor topic are taken from “employee benefits, pensions, and retirement accounts,” whereas “drug” for the Law & crime topic is derived from “illegal drug crime and enforcement, criminal penalties for drug crimes.”