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Applied Scaling & Classification Techniques in Political Science (2017/18)
Overview of the course |
Syllabus (English) (Japanese)
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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: Text as Data
Lab Class: How to analyze texts with the Quanteda package. Read this file before our Lab 1!!! (scripts: a) Lab 1 main script; b) Lab 1 extra script; datasets: a) Trump tweets sample; b) Inaugural US Presidential speeches sample; c) List President USA by party)
Assignment 1
Second Lecture
Morning class: From words to positions (Wordscores)
Lab Class: How to implement the Wordscores algorithm using the Quanteda package (script ; dataset)
Assignment 2 (dataset for the assignment)
Third Lecture
Morning class: From words to positions (Wordfish)
Lab Class: How to implement the Wordfish algorithm using the Quanteda packages (packages to install in R before lab; slide; script)
Assignment 3
Fourth Lecture
Morning class: From words to positions: Analyzing Japanese Legislative Speeches
Lab Class: How to recover data from Twitter (packages to install in R before lab (including twitterR & streamR); script about twitteR; script about streamR)
Assignment 4 (dataset for the assignment)
Fifth Lecture
Morning class: From Manifestoes to positions: The Comparative Manifesto Project (Appendix)
Lab Class: How to extract party positions from party Manifestoes (packages to install in R before lab; script 1, script 2)
Assignment 5
Sixth 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; script)
Assignment 6 - a possibile solution script
Seventh Lecture
Morning class: From words to issues: Dictionaries and Supervised Learning Models
Lab Class: Dictionaries & Classifiers (package to install in R before lab; script: dictionaries; script: classifiers; dataset on Trump; dictionary Lexicoder )
Assignment 7 (dataset for Assignment 7) - solution script (second part)
Eight Lecture
Morning class: From words to issues: Supervised Aggregated Learning Models; insights 1: Using social media to now- and fore-cast politics; insights 2: Using social media to understand support towards Isis and its consequences
Lab Class: iSAX & ReadMe (packages to install in R before lab; script 1; script2)
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: Text as Data
Lab Class: How to analyze texts with the Quanteda package. Read this file before our Lab 1!!! (scripts: a) Lab 1 main script; b) Lab 1 extra script; datasets: a) Trump tweets sample; b) Inaugural US Presidential speeches sample; c) List President USA by party)
Assignment 1
Second Lecture
Morning class: From words to positions (Wordscores)
Lab Class: How to implement the Wordscores algorithm using the Quanteda package (script ; dataset)
Assignment 2 (dataset for the assignment)
Third Lecture
Morning class: From words to positions (Wordfish)
Lab Class: How to implement the Wordfish algorithm using the Quanteda packages (packages to install in R before lab; slide; script)
Assignment 3
Fourth Lecture
Morning class: From words to positions: Analyzing Japanese Legislative Speeches
Lab Class: How to recover data from Twitter (packages to install in R before lab (including twitterR & streamR); script about twitteR; script about streamR)
Assignment 4 (dataset for the assignment)
Fifth Lecture
Morning class: From Manifestoes to positions: The Comparative Manifesto Project (Appendix)
Lab Class: How to extract party positions from party Manifestoes (packages to install in R before lab; script 1, script 2)
Assignment 5
Sixth 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; script)
Assignment 6 - a possibile solution script
Seventh Lecture
Morning class: From words to issues: Dictionaries and Supervised Learning Models
Lab Class: Dictionaries & Classifiers (package to install in R before lab; script: dictionaries; script: classifiers; dataset on Trump; dictionary Lexicoder )
Assignment 7 (dataset for Assignment 7) - solution script (second part)
Eight Lecture
Morning class: From words to issues: Supervised Aggregated Learning Models; insights 1: Using social media to now- and fore-cast politics; insights 2: Using social media to understand support towards Isis and its consequences
Lab Class: iSAX & ReadMe (packages to install in R before lab; script 1; script2)