Supervised Modelling

Lecture Slides Lecture Slides (pdf) Lecture Slides (ipynb)

Tutorial Exercise Tutorial Exercise (pdf) Tutorial Exercise (ipynb)

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This week we focus on supervised text classification. We will look at how to create training sets, train classification models, and evaluate their performance.

Required Readings

• Grimmer, Roberts & Stewart Chs 17 An Overview of Supervised Classification, 18 Coding a Training Set, 19 Classifying Documents with Supervised Learning, 20 Checking Performance.
• Pablo Barberá et al. 2021. “Automated Text Classification of News Articles: A Practical Guide.” Political Analysis 29 (1): 19–42. http://pablobarbera.com/static/text_practical_guide.pdf

Additional Readings

• Kenneth Benoit et al. 2016. “Crowd-sourced Text Analysis: Reproducible and Agile Production of Political Data.” American Political Science Review 110 (2): 278–295. https://kenbenoit.net/pdfs/Crowd_sourced_data_coding_APSR.pdf
• Andrew Peterson and Arthur Spirling. 2018. “Classification Accuracy as a Substantive Quantity of Interest: Measuring Polarization in Westminster Systems.” Political Analysis 26 (1): 120–128. https://doi.org/10.1017/pan.2017.39

Tutorial

• Supervised text classification