Text Data

Learning Objective

After this session you will (a) understand the essential problems of working with text as data and their solutions and (b) have a basic understanding of and ability to apply topic and scaling models, and their manual counterparts

Required Readings

  1. Lucas, Christopher et al. 2013. “Computer Assisted Text Analysis for Comparative Politics.”
  2. Grimmer, J, and B M Stewart. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis 21(3): 267–97.

Optional Readings

  1. Roberts, Margaret E. et al. 2014. “Structural Topic Models for Open-Ended Survey Responses.” American Journal of Political Science 58(4): 1064–82.
  2. Proksch, Sven-Oliver, Will Lowe, Jens Wäckerle, and Stuart Soroka. 2019. “Multilingual Sentiment Analysis: A New Approach to Measuring Conflict in Legislative Speeches.” Legislative Studies Quarterly 44(1): 97–131.
  3. 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.
  4. Silge, Julia, and David Robinson. 2020. Tidy Text Mining. O’Reilly Media. https://www.tidytextmining.com/.

Lecture

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