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
- Lucas, Christopher et al. 2013. “Computer Assisted Text Analysis for Comparative Politics.”
- 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
- Roberts, Margaret E. et al. 2014. “Structural Topic Models for Open-Ended Survey Responses.” American Journal of Political Science 58(4): 1064–82.
- 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.
- 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.
- Silge, Julia, and David Robinson. 2020. Tidy Text Mining. O’Reilly Media. https://www.tidytextmining.com/.
Lecture
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