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Applied Scaling & Classification Techniques in Political Science using text data
(academic year 2024/25)
Syllabus
Course aims and objectives
Students will learn how to employ some widely discussed methods advanced in the literature to analyze political texts and to extract from them useful information for texting their own theories.
First Lecture
28/11/24 Theory: Theory: An introduction to text analytics
Reference texts: (1; 2)
28/11/24 Lab class: An introduction to the Quanteda package (a) packages to install for Lab 1; b) script for Lab 1: R script; c) script for Lab 1: Google Colab notebook; datasets: a) Boston tweets sample (.csv; .rds); b) Inaugural US Presidential speeches sample (to open this file, please use the data compression tool WinRAR); c) sample of Japanese legislatives speeches; EXTRA: a) R script to tokenize Japanese & Chinese; b) Google Colab notebook to open files stored in your Google Drive
Second Lecture
5/12/24 Theory: Unsupervised classification methods: the Topic Model (and beyond)
Reference text (2):
First assignment (due: 12 December 2025) (dataset for the first part: Guardian 2013 - .csv; .rds) (dataset for the second part: Trump 2018 tweets. To open this file, use the command readRDS("Trump2018.rds"))
Third Lecture
12/12/24 Theory:
12/12/24 Lab class:
Fourth Lecture
19/12/24 Theory:
19/12/24 Lab class:
Fifth Lecture
9/1/24 Theory:
9/1/24 Lab class:
Sixth Lecture
16/1/24 Theory:
16/1/24 Lab class:
Seventh Lecture
23/1/24 Theory:
23/1/24 Lab class:
Students will learn how to employ some widely discussed methods advanced in the literature to analyze political texts and to extract from them useful information for texting their own theories.
First Lecture
28/11/24 Theory: Theory: An introduction to text analytics
Reference texts: (1; 2)
- Grimmer, Justin, and Stewart, Brandon M. 2013. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3): 267-297
- Benoit, Kenneth (2020). Text as data: An overview. In Luigi Curini and Robert Franzese (eds.), SAGE Handbook of Research Methods is Political Science & International Relations, London, Sage, chapter 26
28/11/24 Lab class: An introduction to the Quanteda package (a) packages to install for Lab 1; b) script for Lab 1: R script; c) script for Lab 1: Google Colab notebook; datasets: a) Boston tweets sample (.csv; .rds); b) Inaugural US Presidential speeches sample (to open this file, please use the data compression tool WinRAR); c) sample of Japanese legislatives speeches; EXTRA: a) R script to tokenize Japanese & Chinese; b) Google Colab notebook to open files stored in your Google Drive
Second Lecture
5/12/24 Theory: Unsupervised classification methods: the Topic Model (and beyond)
Reference text (2):
- Grimmer, Justin, and Stewart, Brandon M. 2013. Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3): 267-297
- Robert, Margaret E., Brandon M. Stewart, Dustin Tingley, Christopher Luca, Jetson Leder-Luis, Shana Kushner Gadarian, Bethany Albertson, David G. Rand. 2014. Structural Topic Models for Open-Ended Survey Response. American Journal of Political Science, 58(4), 1064-1082
First assignment (due: 12 December 2025) (dataset for the first part: Guardian 2013 - .csv; .rds) (dataset for the second part: Trump 2018 tweets. To open this file, use the command readRDS("Trump2018.rds"))
Third Lecture
12/12/24 Theory:
12/12/24 Lab class:
Fourth Lecture
19/12/24 Theory:
19/12/24 Lab class:
Fifth Lecture
9/1/24 Theory:
9/1/24 Lab class:
Sixth Lecture
16/1/24 Theory:
16/1/24 Lab class:
Seventh Lecture
23/1/24 Theory:
23/1/24 Lab class: