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授業情報

開講年度 2017年度 開講箇所 大学院政治学研究科
科目名
Advanced Topics in Political Science: Polimetrics - Applied Scaling & Classification Techniques in Political Science(PS・Curini)

担当教員 クリーニ ルイージ
学期曜日時限 冬クォーター  水2-3
科目区分 政治学専攻設置科目 配当年次 1年以上 単位数 2
使用教室 3-903(学部PCルーム) キャンパス 早稲田
科目キー 31SGU31005 科目クラスコード 01
授業で使用する言語 英語
  コース・コード POLX611L
大分野名称 政治学
中分野名称 政治学
小分野名称 現代政治/政治過程
レベル 修士レベル 授業形態 講義

シラバス情報

最終更新日時:2017/09/05 16:22:29

授業概要 Therecent years have witnessed a dramatic increase in the interest towards theanalysis of texts in social sciences. This is largely due to the development ofnew methods that facilitate substantively important inferences about politicsfrom large text collections. This course aims to provide an introductory guideto this exciting new area of research, while also offering guidelines on how toeffectively use text methods for social scientific research. The attention willbe devoted to three main areas: 1) automated content methods that allow toestimate the location of actors in policy space (and the differences betweenthese methods and the human-coded ones); 2) automated content methods thatallow to organize texts into a set of pre-defined categories (with a particularemphasis on social media texts); 3) automated content methods that allow todiscover new ways of organizing texts into a set of unknown categories. Labsessions are a crucial part of the course: they are offered for “hands-on”experiences to learn the techniques discussed during the morning class. Anelementary knowledge of R, plus a curiosity towards applied statistics , aregood prerequisites for the lab sessions. All the datasets and replication filesof the lab sessions will be made available at a dedicated URL.
授業の到達目標 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.
授業計画

1.Morning class (Theory): An introduction to textualanalysis

2.Afternoon class (Lab): How to begin to analyze texts withthe Quanteda package

3.Morning class (Theory): From words to positions (Wordscores)

4.Afternoon class (Lab): How to implement the Wordscoresalgorithm using the Austin and the Quanteda packages

5.Morning class (Theory): From words to positions(Wordfish)

6.Afternoon class (Lab): How to implement the Wordfishalgorithm using the Austin and the Quanteda packages (part 1)

7.Morning class (Theory): From words to positions:Analyzing Japanese Legislative Speeches

8.Afternoon class (Lab): How to implement the Wordfishalgorithm using the Austin and the Quanteda packages (part 2)

9.Morning class (Theory): From Manifestoes to positions:The Comparative Manifesto Project

10.Afternoon class (Lab): How to extract party positionsfrom party Manifestoes

11.Morning class (Theory): From words to issues: Thestructural topic Model

12.Afternoon class (Lab): How to implement the stm package

13.Morning class (Theory): From social media to information

14.Afternoon class (Lab): How to run a sentimentanalysis using the iSAX package (part 1)
15.Morning class (Theory): Using social media tounderstand Islamic terrorism and nowcasting politics
16Afternoon class (Lab):  How to run a sentiment analysis using theiSAX package (part 2)
教科書 Please see below for the references.
参考文献 Classes 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.
Classes 3 & 4: 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
Lowe, Will (2008). Understanding Wordscores, Political Analysis, 16(4): 356-371
Classes 5, 6, 7 & 8: 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.
Classes 9 & 10: Lowe, W., Benoit, K., Mikhaylov, S., & Laver, M. (2011). Scaling Policy Preferences From Coded Political Texts. Legislative Studies Quarterly, 26(1), 123-155.
Classes 11 & 12: 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
Lucas, Christopher, Richard A. Nielsen, Margaret E. Roberts, Brandon M. Stewart, Alex Storer, Dustin Tingley (2015). Computer-Assisted Text Analysis for Comparative Politics, Political Analysis, 23, 254-277
Classes 13 & 14: Curini Luigi, Andrea Ceron, and Stefano M. Iacus (2017). Politics and Big Data: Nowcasting and Forecasting Elections with Social Media. London: Routledge, 2017, Chapter 2
Curini Luigi, Andrea Ceron, and Stefano M. Iacus. “iSA: a fast, scalable and accurate algorithm for sentiment analysis of social media content”, Information Sciences, 367–368 (1), 2016, 105–124
Classes 15 & 16: Curini Luigi, Andrea Ceron, and Stefano M. Iacus (2017). Politics and Big Data: Nowcasting and Forecasting Elections with Social Media. London: Routledge, 2017, Chapter 5
成績評価方法
割合 評価基準
レポート: 85% Course grades will be based on home-assignments and class-participation.
平常点評価: 15% Course grades will be based on home-assignments and class-participation.
備考・関連URL http://www.luigicurini.com/applied-scaling--classification-techniques-in-political-science.html

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