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Applied Scaling & Classification Techniques in Political Science (2020/21)
IMPORTANT INFO
This course will be conducted online using Zoom (Join Zoom Meeting https://zoom.us/j/2335812074?pwd=aitDcWNFd1g3QVR1NHIxMit1bWpzUT09). If you have any problem to access via Zoom, please contact me a few days before the course starts. Please visit this website in the next weeks for details on the course
Office-hours: since the 1st December 2020 every Tuesday on Zoom 5:00-7:00 pm (Japanese time). Plz write me in advance to fix an appointment
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
11/25/20 Wednesday class (Theory): An introduction to text analytics (first part; second part)
Reference texts: (1; 2)
11/26/20 Thursday class (Lab): An introduction to the Quanteda package (scripts: a) packages to install; b) Lab 1 script; datasets: a) Boston tweets sample; b) Inaugural US Presidential speeches sample (to open this file, please use the data compression tool WinRAR); c) List President USA by party
First assignment (due: 1 December 2020)
Second Lecture
12/02/20 Wednesday class (Theory): From words to positions: unsupervised scaling models
Reference texts (1; 2; 3)
12/03/20 Thursday class (Lab): How to implement the Wordfish algorithm (scripts: a) packages to install; b) Lab 2 script (part I); c) Lab 2 script (part II); datasets: a) UK party programs 1992 and 1997; b) sample of Japanese legislatives speeches (to open these files, please use the data compression tool WinRAR)
Second assignment (due: 8 December 2020) (datasets for Assignment 1: a) the Irish party manifestoes; b) Japanese legislatives speeches. To open these files, please use the data compressione tool WinRAR)
Third Lecture
12/09/20 Wednesday class (Theory): From words to positions: supervised scaling models
Reference texts (1; 2; 3)
12/10/20 Thursday class (Lab): How to implement the Wordscores algorithm (scripts: a) packages to install; b) Lab 3 script (part I); c) Lab 3 script (part II) )
Third assignment (due: 15 December 2020)
Fourth Lecture
12/16/20 Wednesday class (Theory): From words to issues: unsupervised classification models
Reference text (1):
12/17/20 Thursday class (Lab): How to implement a Topic Model (scripts: a) packages to install; b) Lab 4 script; datasets: a) Guardian 2016; b) Guardian 2013)
Fourth assignment (due: 22 December 2020)
Fifth Lecture
01/06/21 Wednesday class (Theory): (Part 1): From words to issues: structural topic models; (Part 2): Dictionary models
Reference texts (1, 2):
01/07/21 Thursday class (Lab): How to implement a Structural Topic Model and dictionary models (scripts: a) packages to install; b) Lab 5 script: STM; c) Lab 5 dictionaries; d) Lab 5 dictionaries and Twitter; dataset: a) NyT; b) data for topical content analysis)
Fifth Assignment (due: 13 January 2021)
Sixth Lecture
01/13/21 Wednesday class (Theory): (Part 1): From words to issues: semi-supervised classification models; (Part 2): An introduction to supervised classification models
Reference texts (1, 2, 3, 4, 5):
01/14/21 Thursday class (Lab): How to implement a semi-supervised classification model (scripts: a) packages to install; b) Lab 6 script: Newsmap; c) Lab 6 script: KeyATM)
Sixth Assignment (due: 20 January 2021)
Seventh Lecture
01/20/21 Wednesday class (Theory): From words to issues: supervised classification models
Reference text (1):
01/21/21 Thursday class (Lab): How to implement supervised classification models (scripts: a) packages to install; b) Lab 7 script; datasets: a) movie reviews training-set; b) movie reviews test-set; c) Nationality)
Seventh Assignment (due: 27 January 2021) (datasets for Assignment 7: a) UK training set; b) UK test set)
Eighth Lecture
01/27/21 Wednesday class (Theory): How to validate the results you get from machine learning algorithms
Reference text (1, 2, 3):
01/28/21 Thursday class (Lab): How to apply k-fold cross validation (scripts: a) package to install; b) Lab 8 script (part A); c) Lab 8 script (part B); d) Lab 8 script (part C)
Eigth Assignment (due: 3 February 2021)
This course will be conducted online using Zoom (Join Zoom Meeting https://zoom.us/j/2335812074?pwd=aitDcWNFd1g3QVR1NHIxMit1bWpzUT09). If you have any problem to access via Zoom, please contact me a few days before the course starts. Please visit this website in the next weeks for details on the course
Office-hours: since the 1st December 2020 every Tuesday on Zoom 5:00-7:00 pm (Japanese time). Plz write me in advance to fix an appointment
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
11/25/20 Wednesday class (Theory): An introduction to text analytics (first part; second part)
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
11/26/20 Thursday class (Lab): An introduction to the Quanteda package (scripts: a) packages to install; b) Lab 1 script; datasets: a) Boston tweets sample; b) Inaugural US Presidential speeches sample (to open this file, please use the data compression tool WinRAR); c) List President USA by party
First assignment (due: 1 December 2020)
Second Lecture
12/02/20 Wednesday class (Theory): From words to positions: unsupervised scaling models
Reference texts (1; 2; 3)
- Proksch, Sven-Oliver, and Slapin, Jonathan B. 2008. A Scaling Model for Estimating Time-Series Party Positions from Texts. American Journal of Political Science, 52(3): 705-722.
- Proksch, Sven-Oliver, and Slapin, Jonathan B. 2009. How to Avoid Pitfalls in Statistical Analysis of Political Texts: The Case of Germany. German Politics, 18(3): 323-344.
- Curini, Luigi, Hino, Airo, and Atsushi Osaki. 2020. Intensity of government–opposition divide as measured through legislative speeches and what we can learn from it. Analyses of Japanese parliamentary debates, 1953–2013, Government and Opposition, 55(2), 184-201
12/03/20 Thursday class (Lab): How to implement the Wordfish algorithm (scripts: a) packages to install; b) Lab 2 script (part I); c) Lab 2 script (part II); datasets: a) UK party programs 1992 and 1997; b) sample of Japanese legislatives speeches (to open these files, please use the data compression tool WinRAR)
Second assignment (due: 8 December 2020) (datasets for Assignment 1: a) the Irish party manifestoes; b) Japanese legislatives speeches. To open these files, please use the data compressione tool WinRAR)
Third Lecture
12/09/20 Wednesday class (Theory): From words to positions: supervised scaling models
Reference texts (1; 2; 3)
- Laver, Michael, Kenneth Benoit, John Garry. 2003. Extracting Policy Positions from political texts using words as data. American Political Science Review, 97(02), 311-331
- Egerod, Benjamin C.K., and Robert Klemmensen (2020). Scaling Political Positions from text. Assumptions, Methods and Pitfalls. In Luigi Curini and Robert Franzese (eds.), SAGE Handbook of Research Methods is Political Science & International Relations, London, Sage, chapter 27
- Martin, Lanny W., and Georg Vanberg. 2008. A robust transformation procedure for interpreting political text. Political Analysis, 16: 93-100
12/10/20 Thursday class (Lab): How to implement the Wordscores algorithm (scripts: a) packages to install; b) Lab 3 script (part I); c) Lab 3 script (part II) )
Third assignment (due: 15 December 2020)
Fourth Lecture
12/16/20 Wednesday class (Theory): From words to issues: unsupervised classification models
Reference text (1):
- 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
12/17/20 Thursday class (Lab): How to implement a Topic Model (scripts: a) packages to install; b) Lab 4 script; datasets: a) Guardian 2016; b) Guardian 2013)
Fourth assignment (due: 22 December 2020)
Fifth Lecture
01/06/21 Wednesday class (Theory): (Part 1): From words to issues: structural topic models; (Part 2): Dictionary models
Reference texts (1, 2):
- Roberts, Margaret E., Brandon M. Stewart, Dustin Tingley. 2014. STM: R Package for Structural Topic Models. Journal of Statistical Software, https://cran.r-project.org/web/packages/stm/vignettes/stmVignette.pdf
- 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
01/07/21 Thursday class (Lab): How to implement a Structural Topic Model and dictionary models (scripts: a) packages to install; b) Lab 5 script: STM; c) Lab 5 dictionaries; d) Lab 5 dictionaries and Twitter; dataset: a) NyT; b) data for topical content analysis)
Fifth Assignment (due: 13 January 2021)
Sixth Lecture
01/13/21 Wednesday class (Theory): (Part 1): From words to issues: semi-supervised classification models; (Part 2): An introduction to supervised classification models
Reference texts (1, 2, 3, 4, 5):
- Kohei Watanabe and Yuan Zhou (2020) Theory-Driven Analysis of Large Corpora: Semisupervised Topic Classification of the UN Speeches. Social Science Computer Review, DOI: 10.1177/0894439320907027
- Shusei Eshima, Kosuke Imai, and Tomoya Sasaki (2020). Keyword Assisted Topic Models, arXiv:2004.05964v1
- 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
- Curini, Luigi, and Robert Fahey. 2020. Sentiment Analysis. In: Luigi Curini and Robert Franzese (eds.), Sage Handbook of Research Methods in Political Science and International Relations, London: Sage, chapter 29
- Barberá, Pablo et al. (2020). Automated Text Classification of News Articles: A Practical Guide. Political Analysis, DOI: 10.1017/pan.2020
01/14/21 Thursday class (Lab): How to implement a semi-supervised classification model (scripts: a) packages to install; b) Lab 6 script: Newsmap; c) Lab 6 script: KeyATM)
Sixth Assignment (due: 20 January 2021)
Seventh Lecture
01/20/21 Wednesday class (Theory): From words to issues: supervised classification models
Reference text (1):
- Olivella, Santiago, and Shoub Kelsey (2020). Machine Learning in Political Science: Supervised Learning Models. In Luigi Curini and Robert Franzese (eds.), SAGE Handbook of Research Methods is Political Science & International Relations, London, Sage, chapter 56
01/21/21 Thursday class (Lab): How to implement supervised classification models (scripts: a) packages to install; b) Lab 7 script; datasets: a) movie reviews training-set; b) movie reviews test-set; c) Nationality)
Seventh Assignment (due: 27 January 2021) (datasets for Assignment 7: a) UK training set; b) UK test set)
Eighth Lecture
01/27/21 Wednesday class (Theory): How to validate the results you get from machine learning algorithms
Reference text (1, 2, 3):
- 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
- Cranmer, Skyler J. and Desmarais, Bruce A. (2017) What Can We Learn from Predictive Modeling?, Political Analysis, 25: 145-166
- Curini, Luigi, and Robert Fahey. 2020. Sentiment Analysis. In: Luigi Curini and Robert Franzese (eds.), Sage Handbook of Research Methods in Political Science and International Relations, London: Sage, chapter 29
01/28/21 Thursday class (Lab): How to apply k-fold cross validation (scripts: a) package to install; b) Lab 8 script (part A); c) Lab 8 script (part B); d) Lab 8 script (part C)
Eigth Assignment (due: 3 February 2021)