Online articles
Ban, P., Fouirnaies, A., Hall, A. B., & Snyder, J. M. (2018). How Newspapers Reveal Political Power. Political Science Research and Methods, Online first.
Benoit, K., Conway, D., Lauderdale, B. E., Laver, M., & Mikhaylov, S. (2016). Crowd-sourced text analysis: reproducible and agile production of political data. American Political Science Review, 110 (02), 278-295
Benoit, Kenneth et. al. (2017). "quanteda: Quantitative Analysis of Textual Data". R package version: 0.9.9-53.
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.
Denny, M. J., & Spirling, A. (2018). Text preprocessing for unsupervised learning: why it matters, when it misleads, and what to do about it. Political Analysis, 1-22.
Grimmer, J. & Stewart, B. M. (2013). Text as data: the promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3), 267–297.
Grimmer, J. (2010). A Bayesian hierarchical topic model for political texts: measuring expressed agendas in senate press releases. Political Analysis, 18(1), 1–35.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. New York: Springer. Chapters: 2.1, 2.2, 5.1, and 6.2.
Lang, D. T. (2007). R as a web client–the rcurl package. Journal of Statistical Software
Laver, M., Benoit, K., & Garry, J. (2003). Extracting policy positions from political texts using words as data. American Political Science Review, 97 (02), 311–331.
Mikhaylov, S., Laver, M., & Benoit, K. R. (2012). Coder reliability and misclassification in the human coding of party manifestos. Political Analysis, 20(1), 78–91.
Roberts, C. W. (2000). A conceptual framework for quantitative text analysis. Quality and Quantity, 34(3), 259–274.
Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder‐Luis, J., Gadarian, S. K., ... & Rand, D. G. (2014). Structural Topic Models for Open‐Ended Survey Responses. American Journal of Political Science, 58 (4), 1064-1082.
Slapin, J. B. & Proksch, S.-O. (2008). A scaling model for estimating time-series party positions from texts. American Journal of Political Science, 52(3), 705–722.
Silge, J., & Robinson, D. (2017) Text Mining with R: A Tidy Approach
Welbers, K., van Atteveldt, W., & Benoit, K. (2017). Text Analysis in R. Communication Methods & Measures, 11.
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