Computer-Assisted Text Analysis for Comparative Politics

By Christopher Lucas, Richard A. Nielsen, Margaret E. Roberts, Brandon M Stewart, Alex Storer, Dustin Tingley. Political Analysis, 2015

468 citations recorded by the source corpus.

Subfields: General Social Sciences, Political Science and International Relations

View Paper: https://www.semanticscholar.org/paper/ca1469279ace2e341fd385c884129367e1b7dda7

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In General Social Sciences

In Political Science and International Relations

Citations in the corpus

stm: An R Package for Structural Topic Models (2019, methodology): “In this paper, we provide brief interpretations of results, and we direct readers to the companion papers (Roberts et al. 2013, 2014; Lucas et al. 2015) for complete applications.”
Structural Topic Models for Open‐Ended Survey Responses (2014, methodology): “In a companion paper, we provide a thorough contrast of our method to supervised learning techniques (Lucas et al. 2013).”
Quantitative analysis of large amounts of journalistic texts using topic modelling (2016, methodology): “Lucas et al. (2015) apply topic modelling to different types of documents such as fatwas and social media posts in order to facilitate comparison between countries.”
Comprehensive stereotype content dictionaries using a semi‐automated method (2020, background): “Language and text analysis have a long history in psychology (e.g., Dewey, 1910; Miller, 1951; see Boyd, 2017 for a review) and affiliated fields ranging from Sociology and Political Science (see Lucas et al., 2015) to Computer Science (see Nerbonne, 2003).”
A Model of Text for Experimentation in the Social Sciences (2016, methodology): “In related work, we have applied these methods to study open-ended survey responses (Roberts et al. 2014b), comparative politics literature (Lucas et al. 2015), and studentgenerated text in massive open online courses (Reich et al. 2015).”
Twitter as Data (2018, background): “Computationally analyzing text is a growing field in the social sciences (Grimmer and Stewart, 2013; Lucas et al., 2015), and Twitter provides scholars with large corpora.”