Applied Scaling & Classification Techniques in Political Science (2018/19)

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 
Morning class: An introduction to textual analysis methods
Lab Class: How to analyze texts with the Quanteda package. Read this file before our Lab 1!!! (scripts: a) Lab 1 script; datasets: a) Boston tweets sample; b) Inaugural US Presidential speeches sample (to open this file, please use the data compressione tool WinRAR); c) List President USA by party; d) sample of Japanese legislatives speeches (to open this file, please use the data compressione tool WinRAR))

Second Lecture
Morning class:  From words to positions (Wordscores)
Lab Class: How to implement the Wordscores algorithm using the Quanteda package (scripts: a) Lab 2 script; b) Lab 2 script for the 3 exercises discussed in the lab; datasets: a) UK party programs 1992 and 1997: to open this file, please use the data compressione tool WinRA) 
Assignment 1 (dataset for Assignment 1: the Irish party manifestoes; to open this file, please use the data compressione tool WinRAR)
Assignment 1 solution (second part)

Third Lecture
Morning class: From words to positions (Wordfish) 
Lab Class: How to implement the Wordfish algorithm using the Quanteda package (packages to install in R before lab; Lab 3 script; Slide about Lab 3)
Assignment 2
Assignment 2 solution

Fourth Lecture
Morning class: From Manifestoes to positions: The Comparative Manifesto Project  (Appendix: the theory behind CMP)
Lab Class: How to extract party positions from party Manifestoes (packages to install in R before lab; scripts: a) Lab 4 script part 1; b) Lab 4 script part 2)
Assignment 3
Assignment 3 solution

Fifth Lecture
Morning class: From words to issues: The structural topic model
Lab Class: How to implement the stm package (packages to install in R before lab; If you have any problems to install the package «stmBrowser», please download it from here, and install it on R as «local files»; scripts: a) Lab 5 script; b) Lab 5 extra script)
Assignment 4
Assignment 4 solution

Sixth Lecture
Morning class: Dictionaries and Supervised classification methods
Lab Class: How to implement Dictionaries and Supervised classification methods (packages to install in R before lab); some notes; scripts: a) dictionaries; b) classifiers. Dataset for the lab class (dataset as a .rar file); packages to install in R before next-week Lab!
Assignment 5 (dataset; dataset as a .rar file)
Assignment 5 solution (part 1; part 2 (version A); part 2 (version B) )

Seventh Lecture
Morning class: How to retrieve information from Twitter (part 1): script 1; script 2
Lab Class: How to retrieve information from Twitter (part 2): script 3; packages to install in R before next-week Lab: packages part 1; packages part 2
Assignment 6
Assignment 6 solution

Eight Lecture
​Morning class: Supervised aggregated classification methods. Lab script
Lab Class: Network analysis: a short introduction. Lab script: Part 1; Part 2; Dataset of Letters