References

Bécue-Bertaut, Monica. 2019. Textual Data Science with R. CRC Press.

Benoit, Kenneth. 2020. “Text as Data: An Overview.” In Handbook of Research Methods in Political Science and International Relations, edited by Luigi Curini and Robert Franzese, 461–97. Thousand Oaks: Sage.

Benoit, Kenneth, and Akitaka Matsuo. 2020. Spacyr: Wrapper to the ’spaCy’ ’NLP’ Library. https://CRAN.R-project.org/package=spacyr.

Benoit, Kenneth, Kevin Munger, and Arthur Spirling. 2019. “Measuring and Explaining Political Sophistication Through Textual Complexity.” American Journal of Political Science 63 (2): 491–508. https://doi.org/10.1111/ajps.12423.

Benoit, Kenneth, Kohei Watanabe, Haiyan Wang, Paul Nulty, Adam Obeng, Stefan Müller, and Akitaka Matsuo. 2018. “Quanteda: An R Package for the Quantitative Analysis of Textual Data.” Journal of Open Source Software 3 (30): 774. https://doi.org/10.21105/joss.00774.

Boussalis, Constantine, Travis G. Coan, Mirya R. Holman, and Stefan Müller. 2021. “Gender, Candidate Emotional Expression, and Voter Reactions During Televised Debates.” American Political Science Review 115 (4): 1242–57. https://doi.org/10.1017/S0003055421000666.

Castanho Silva, Bruno, and Sven-Oliver Proksch. 2021. “Politicians Unleashed? Political Communication on Twitter and in Parliament in Western Europe.” Political Science Research and Methods published ahead of print (doi: 10.1017/psrm.2021.36). https://doi.org/10.1017/psrm.2021.36.

Chang, Jonathan. 2015. Lda: Collapsed Gibbs Sampling Methods for Topic Models. https://CRAN.R-project.org/package=lda.

Crisp, Brian F., Benjamin Schneider, Amy Catalinac, and Taishi Muraoka. 2021. “Capturing Vote-Seeking Incentives and the Cultivation of a Personal and Party Vote.” Electoral Studies 72: 102369. https://doi.org/10.1016/j.electstud.2021.102369.

Denny, Matthew W., and Arthur Spirling. 2018. “Text Preprocessing for Unsupervised Learning: Why It Matters, When It Misleads, and What to Do about It.” Political Analysis 26 (2): 168–89. https://doi.org/10.1017/pan.2017.44.

Eshima, Shusei, Kosuke Imai, and Tomoya Sakasi. 2023. “Keyword Assisted Topic Models.” American Journal of Political Science online first. https://doi.org/10.1111/ajps.12779.

Feinerer, Ingo, Kurt Hornik, and David Meyer. 2008a. “Text Mining Infrastructure in R.” Journal of Statistical Software 25 (5): 1–54. https://doi.org/10.18637/jss.v025.i05.

———. 2008b. “Text Mining Infrastructure in R.” Journal of Statistical Software 25 (5): 1–54. https://www.jstatsoft.org/v25/i05/.

Gessler, Theresa, and Sophia Hunger. 2022. “How the Refugee Crisis and Radical Right Parties Shape Party Competition on Immigration.” Political Science Research and Methods 10 (3): 524–44. https://doi.org/10.1017/psrm.2021.64.

Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. 2022. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts: A New Framework for Machine Learning and the Social Sciences. Princeton: Princeton University Press.

Grün, Bettina, and Kurt Hornik. 2011. “topicmodels: An R Package for Fitting Topic Models.” Journal of Statistical Software 40 (13): 1–30. https://doi.org/10.18637/jss.v040.i13.

Herzog, Alexander, and Kenneth Benoit. 2015. “The Most Unkindest Cuts: Speaker Selection and Expressed Goverment Dissent During Economic Crisis.” The Journal of Politics 77 (4): 1157–75. https://doi.org/10.1086/682670.

Honnibal, Matthew, Ines Montani, Sophie Van Landeghem, and Adriane Boyd. 2020. “spaCy: Industrial-Strength Natural Language Processing in Python.” https://doi.org/10.5281/zenodo.1212303.

Hvitfeldt, Emil, and Julia Silge. 2021. Supervised Machine Learning for Text Analysis in r. Boca Raton: CRC Press. https://smltar.com.

Kwartler, Ted. 2017. Text Mining in Practice with R. Chichester: John Wiley & Sons.

Lupia, Arthur, Stuart N. Soroka, and Alison Beatty. 2020. “What Does Congress Want from the National Science Foundation? A Content Analysis of Remarks from 1995 to 2018.” Science Advances 6 (33): eaaz6300. https://doi.org/10.1126/sciadv.aaz6300.

Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schütze. 2008. An Introduction to Information Retrieval. New York: Cambridge University Press. https://nlp.stanford.edu/IR-book/.

Monroe, B. L., K. M. Quinn, and M. P. Colaresi. 2008. “Fightin’ Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict.” Political Analysis 16 (4): 372–403.

Monroe, Burt L., and Philip A. Schrodt. 2008. “Introduction to the Special Issue: The Statistical Analysis of Political Text.” Political Analysis 16 (4): 351–55. https://doi.org/10.1093/pan/mpn017.

Mosteller, F., and D. L. Wallace. 1963. “Inference in an Authorship Problem.” Journal of the American Statistical Assocation 58 (302): 275–309.

Mullen, Lincoln A., Kenneth Benoit, Os Keyes, Dmitry Selivanov, and Jeffrey Arnold. 2018. “Fast, Consistent Tokenization of Natural Language Text.” Journal of Open Source Software 3: 655. https://doi.org/10.21105/joss.00655.

Müller, Stefan. 2022. “The Temporal Focus of Campaign Communication.” The Journal of Politics 84 (1): 585–90. https://doi.org/10.1086/715165.

Pang, B., L. Lee, and S. Vaithyanathan. 2002. “Thumbs up? Sentiment Classification Using Machine Learning Techniques.” Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 79–86.

Porter, Martin F. 2001. “Snowball: A Language for Stemming Algorithms.” In.

Rauh, Christopher, Bart Joachim Bes, and Martijn Schoonvelde. 2020. “Undermining, Defusing or Defending European Integration? Assessing Public Communication of European Executives in Times of EU Politicisation.” European Journal of Political Research 59 (2): 397–423. https://doi.org/10.1111/1475-6765.12350.

Roberts, Margaret E., Brandon M. Stewart, and Dustin Tingley. 2019. “stm: An R Package for Structural Topic Models.” Journal of Statistical Software 91 (2): 1–40. https://doi.org/10.18637/jss.v091.i02.

Rodriguez, Pedro L., Arthur Spirling, and Brandon Stewart. 2023. conText: ’A La Carte’ on Text (ConText) Embedding Regression. https://CRAN.R-project.org/package=conText.

Sarica, Serhad, and Jianxi Luo. 2021. “Stopwords in Technical Language Processing.” PLoS One 16 (8): e0254937. https://doi.org/10.1371/journal.pone.0254937.

Schofield, Alexandra, and David Mimno. 2016. “Comparing Apples to Apple: The Effects of Stemmers on Topic Models.” Transactions of the Association for Computational Linguistics 4: 287–300. https://doi.org/10.1162/tacl_a_00099.

Silge, Julia, and David Robinson. 2016. “Tidytext: Text Mining and Analysis Using Tidy Data Principles in r.” Journal of Open Source Software 1 (3). https://doi.org/10.21105/joss.00037.

———. 2017. Text Mining with r: A Tidy Approach. O’Reilly Media, Inc.

Slapin, Jonathan B., and Sven-Oliver Proksch. 2008. “A Scaling Model for Estimating Time-Series Party Positions from Texts.” American Journal of Political Science 52 (3): 705–22. https://doi.org/10.1111/j.1540-5907.2008.00338.x.

Turenne, Nicolas. 2016. Analyse de Données Textuelles Sous r. ISTE Group.

Watanabe, Kohei. 2023. LSX: Semi-Supervised Algorithm for Document Scaling. https://CRAN.R-project.org/package=LSX.

Wickham, Hadley. 2021. Rvest: Easily Harvest (Scrape) Web Pages. https://CRAN.R-project.org/package=rvest.

Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. 2nd ed. Sebastopol: O’Reilly. https://r4ds.hadley.nz.

Wilbur, W. John, and Karl Sirotkin. 1992. “The Automatic Identification of Stop Words.” Journal of Information Science 18 (1): 45–55. https://doi.org/10.1177/01655515920180010.

Young, Lori, and Stuart N. Soroka. 2012. “Affective News: The Automated Coding of Sentiment in Political Texts.” Political Communication 29 (2): 205–31.