Big Data i text mining w politologii

Materiały z seminarium “Big Data i text mining w politologii” (WNPiD UAM, 18 czerwca 2019 r.).

Przykładowe skrypty w języku R

Przydatne linki:


  • David M. Blei, Andrew Y. Ng, Michael I. Jordan (2003), Latent Dirichlet Allocation, Journal of Machine Learning Research 3.
  • Ken Benoit et al (2018), quanteda: An R Package for the Quantitative Analysis of Textual Data, Journal of Open Source Software 3 (30).
  • Kenneth Benoit et al (2016), Crowd-Sourced Text Analysis: Reproducible and Agile Production of Political Data, American Political Science Review 110 (2).
  • Shu-Heng Chen (red.) (2018), Big Data in Computational Social Science and Humanities.
  • Matthew W. Denny, Arthur Spirling (2018), Text Preprocessing for Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It, Political Analysis 26 (2).
  • Justin Grimmer, Brandon M. Stewart (2013), Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts, Political Analysis 21 (3).
  • Tomasz Górecki (2011), Podstawy statystyki z przykładami w R.
  • Gareth James et al (2017), An Introduction to Statistical Learning with Applications in R.
  • Ted Kwartler (2017), Text Mining in Practice with R.
  • Michael Laver, John Garry (2000), Estimating Policy Positions from Political Texts, American Journal of Political Science 44 (3).
  • Michael Laver, Kenneth Benoit and John Garry (2003), Extracting Policy Positions from Political Texts Using Words as Data, American Political Science Review 97 (2).
  • Margaret E. Roberts et al (2014), Structural Topic Models for Open-Ended Survey Responses, American Journal of Political Science 58 (4).
  • Jonathan B. Slapin, Sven-Oliver Proksch (2008), A Scaling Model for Estimating Time-Series Party Positions from Texts, American Journal of Political Science 52 (3).
  • Kasper Welbers, Wouter Van Atteweld, Ken Benoit (2017), Text Analysis in R, Communication Methods and Measures 11 (4).
  • John Wilkerson, Andreu Casas (2017), Large-Scale Computerized Text Analysis in Political Science: Opportunities and Challenges, Annual Review of Political Science 20.