Scaling Politically Meaningful Dimensions Using Texts and Votes
By Benjamin E Lauderdale, T. Clark. 2014
141 citations recorded by the source corpus.
Subfields: Artificial Intelligence, General Social Sciences, Sociology and Political Science
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Daniel Preotiuc-Pietro
- Rafael Valle
- Vasileios Lampos
- Nikolaos Aletras
- Jason Poulos
- Dimitrios Tsarapatsanis
- Wei Ping
- Bryan Catanzaro
In General Social Sciences
- Daniel Preotiuc-Pietro
- Rafael Valle
- Vasileios Lampos
- Nikolaos Aletras
- Jason Poulos
- Dimitrios Tsarapatsanis
In Sociology and Political Science
- Daniel Preotiuc-Pietro
- Rafael Valle
- Vasileios Lampos
- Nikolaos Aletras
- Jason Poulos
- Dimitrios Tsarapatsanis
Citations in the corpus
- Missing Data Imputation for Supervised Learning (2016, methodology): “geneous treatment effects (Imai and Strauss, 2011; Green and Kern, 2012; Imai et al., 2013; Grimmer et al., 2014), text analysis (Quinn et al., 2010; Hopkins and King, 2010; Grimmer and Stewart, 2013; Lauderdale and Clark, 2014; Wilkerson et al., 2015), and record linkage (Giraud-Carrier et al., 2010”
- Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective (2016, methodology): “Also, a text mining approach67 utilises sources of metadata about judge’s votes to estimate the degree to which those votes are about68 common issues (Lauderdale and Clark, 2014).”