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

By Justin Grimmer. Political Analysis, 2010

588 citations recorded by the source corpus.

Subfields: General Social Sciences, Political Science and International Relations, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/b06056c20f4ed118e7db9f4e35e674570b2cf8f6

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Citations in the corpus

Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It (2017, methodology): “A commonly used rule of thumb is to discard terms that appear in less than 0.5-1% of documents (Grimmer, 2010; Yano, Smith and Wilkerson, 2012; Grimmer and Stewart, 2013), however, there has been no systematic study of the eﬀects this preprocessing choice has on downstream analyses.”
Can Large Language Models Transform Computational Social Science? (2023, background): “Political scientists study how political actors move agendas (Grimmer, 2010) by persuasively framing their discourse “to promote a particular problem definition, causal interpretation, moral evaluation, and/or treatment recommendation” (Entman, 1993).”
The Evolution of 10-K Textual Disclosure: Evidence from Latent Dirichlet Allocation (2017, background): “and indicated that redundancies between FASB and SEC disclosure requirements will be a focus going forward (Higgins, 2014; SEC, 1998, 2013). Cazier and Pfeiffer (2015b) show that redundant disclosure leads to less efficient price discovery, while Brown and Tucker (2011) find”
An Introduction to Bayesian Inference via Variational Approximations (2011, methodology): “This is similar to the postmodel checks required to establish the utility of ﬁnite mixture models (such as in Quinn et al. 2010 and Grimmer 2010) and factor analytic models (such as Clinton, Jackman, and Rivers 2004 and Ansolabehere, Rodden, and Snyder 2008).”
How Words and Money Cultivate a Personal Vote: The Effect of Legislator Credit Claiming on Constituent Credit Allocation (2012, methodology): “…press releases House members issued over the six-year period.2 Press releases are an incredibly useful tool to measure what legislators are saying to constituents—they are regularly used by House members and have been shown to capture credibly legislators’ behavior in other areas (Grimmer 2010).”
Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures (2014, methodology): “Quinn et al. (2010) and Grimmer (2010) argue that if topics are valid, external events should explain sudden increases in attention to a topic.”
A Model of Text for Experimentation in the Social Sciences (2016, methodology): “Written documents provide a valuable source of data for the measurement of latent linguistic, political, and psychological variables (e.g., Socher et al. 2009; Grimmer 2010; Quinn et al. 2010; Grimmer and Stewart 2013).”
The Security of Latent Dirichlet Allocation (2015, background): “The last few years have witnessed the wide adoption of latent topic modeling, exemplified by latent Dirichlet allocation (LDA), in science and art such as political analysis (Grimmer 2010), business intelligence (Mahajan, Dey & Haque 2008), music (Cai, Zhang, Wang, Zhang & Ma 2007) and even archaeol”
Computational Grounded Theory: A Methodological Framework (2020, background): “Other disciplines in the social sciences, including political scientists (Grimmer 2010; King, Pan, and Roberts 2013; Schwartz and Ungar 2015) and psychologists (Tausczik and Pennebaker 2010; Yu and Ho 2014), have already incorporated computational methods into their repertoire, adapting a range of…”
Structural Topic Models for Open‐Ended Survey Responses (2014, methodology): “Specifically, we contrast the benefits of the STM with vanilla LDA (Blei 2012), factor analysis (Simon and Xenos 2004), and single-membership models (Grimmer 2010; Quinn et al. 2010).”
Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation (2014, methodology): “To measure MI, we employ the information theoretic concept of entropy (Grimmer 2010; MacKay 2003).”
Text as Data (2017, background): “The model has been especially useful in political science (e.g., Grimmer 2010), where researchers have been successful in attaching political issues and beliefs to the estimated latent topics.”
The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings (2018, background): “The analysis of large digitized corpora has already proven fruitful in a range of social scientific endeavors including analysis of discourse surrounding political elections and social movements, the accumulation of knowledge in the production of science, and communication and collaboration within o”
Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology (2018, methodology): “While (intrinsic) topic coherence measures how frequently the top words of a topic co-occur (Mimno et al., 2011), mutual information aims to identify which of the top words contributes the most significant information to a given topic (e.g., Grimmer, 2010).”