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Big Data Analytics (second term 2020/21)
Overview of the course
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
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
1/11/21 Theory class: An introduction to text analytics (first part; second part)
Reference texts: (1; 2, 3)
1/12/21 Lab class: An introduction to the Quanteda package (a) packages to install; b) Lab 1 slides; scripts: 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)
First assignment (due: 18 January 2021)
Second Lecture
1/18/21 Theory class: From words to positions: unsupervised scaling models
Reference texts (1; 2; 3)
1/19/21 Lab class: How to implement the Wordfish algorithm (scripts: a) packages to install; b) Lab 2 script (part I); c) Lab 2 script (part II); d) Lab 2 script (part III); dataset: a) sample of Japanese legislatives speeches (to open these files, please use the data compression tool WinRAR); b) UK party programs 1992 and 1997 (to open this file, please use the data compression tool WinRAR)
Second assignment (due: 25 January 2021) (datasets for Assignment 1: the Irish party manifestoes. To open this file, please use the data compression tool WinRAR)
Third Lecture
1/25/21 Theory class: From words to positions: Supervised scaling models
Reference texts (1; 2; 3)
1/27/21 Lab class: 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: 1 February 2021)
Fourth Lecture
2/1/21 Theory class: From words to issues: unsupervised classification models
Reference text (1):
2/2/21 Lab class: 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: 8 February 2021)
Fifth Lecture
2/8/21 Theory class:
2/10/21 Lab class:
Sixth Lecture
2/15/21 Theory class:
2/16/21 Lab class:
Seventh Lecture
2/22/21 Theory class:
2/23/21 Lab class:
Eighth Lecture
3/1/21 Theory class:
3/2/21 Lab class:
Ninth Lecture
3/8/21 Theory class:
3/9/21 Lab class:
Tenth Lecture
3/15/21 Theory class:
3/16/21 Lab class:
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
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
1/11/21 Theory class: An introduction to text analytics (first part; second part)
Reference texts: (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
- 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
- Grossman, Jonathan, and Pedahzur Ami (2020). Political Science and Big Data: Structured Data, Unstructured Data, and How to Use Them, Political Science Quarterly, 135(2): 225-257
1/12/21 Lab class: An introduction to the Quanteda package (a) packages to install; b) Lab 1 slides; scripts: 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)
First assignment (due: 18 January 2021)
Second Lecture
1/18/21 Theory class: From words to positions: unsupervised scaling models
Reference texts (1; 2; 3)
- Proksch, Sven-Oliber, 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-Oliber, 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. 2018. 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
1/19/21 Lab class: How to implement the Wordfish algorithm (scripts: a) packages to install; b) Lab 2 script (part I); c) Lab 2 script (part II); d) Lab 2 script (part III); dataset: a) sample of Japanese legislatives speeches (to open these files, please use the data compression tool WinRAR); b) UK party programs 1992 and 1997 (to open this file, please use the data compression tool WinRAR)
Second assignment (due: 25 January 2021) (datasets for Assignment 1: the Irish party manifestoes. To open this file, please use the data compression tool WinRAR)
Third Lecture
1/25/21 Theory class: 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
1/27/21 Lab class: 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: 1 February 2021)
Fourth Lecture
2/1/21 Theory class: 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
2/2/21 Lab class: 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: 8 February 2021)
Fifth Lecture
2/8/21 Theory class:
2/10/21 Lab class:
Sixth Lecture
2/15/21 Theory class:
2/16/21 Lab class:
Seventh Lecture
2/22/21 Theory class:
2/23/21 Lab class:
Eighth Lecture
3/1/21 Theory class:
3/2/21 Lab class:
Ninth Lecture
3/8/21 Theory class:
3/9/21 Lab class:
Tenth Lecture
3/15/21 Theory class:
3/16/21 Lab class: