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A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases

. Political Analysis, 18 (1): 1-35 (2010)
DOI: 10.1093/pan/mpp034

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How to Make Causal Inferences Using Texts., , , , and . CoRR, (2018)Deadline Effects in Drug Regulatory Review, , , , , and . (February 2007)Robert Wood Johson Health Policy Scholars Working Paper \# 35.Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond., , , , , , , , , and 3 other author(s). Trans. Assoc. Comput. Linguistics, (2022)Text as data: The promise and pitfalls of automatic content analysis methods for political texts, and . Political Analysis, (2013)Creating and Destroying Party Brands.. LTCSS@ACL, page 2. Association for Computational Linguistics, (2014)Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond., , , , , , , , , and 3 other author(s). CoRR, (2021)A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases. Political Analysis, 18 (1): 1-35 (2010)The representative's problem: how legislators use communication to secure constituent support.. PLEAD@CIKM, page 1-2. ACM, (2013)Dynamic Stochastic Learning in Regulatory Optimization: Deadlines and Error with Continuous and Discrete Evidence, and . (2007)manuscript, Department of Government, Harvard University.TopicCheck: Interactive Alignment for Assessing Topic Model Stability., , , , , , and . HLT-NAACL, page 175-184. The Association for Computational Linguistics, (2015)