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シラバス詳細照会

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授業情報

開講年度 2018年度 開講箇所 政治経済学部
科目名
Advanced Topics in Political Science: Polimetrics - Applied Scaling & Classification Techniques in Political Science

担当教員 クリーニ ルイージ
学期曜日時限 秋クォーター  01:水1時限/02:水5時限
科目区分 演習(専門)以外 配当年次 3年以上 単位数 2
使用教室 01:3-903(学部PCルーム)/02:3-903(学部PCルーム) キャンパス 早稲田
科目キー 11G100SA05 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 POLX301L
大分野名称 政治学
中分野名称 政治学
小分野名称 政治学
レベル 上級レベル 授業形態 講義

シラバス情報

最終更新日時:2018/09/13 18:42:21

授業概要 The recent years have witnessed a dramatic increase in the interest towards the analysis of texts in social sciences. This is largely due to the development of new methods that facilitate substantively important inferences about politics from large text collections. This course aims to provide an introductory guide to this exciting new area of research, while also offering guidelines on how to effectively use text methods for social scientific research. The attention will be devoted to three main areas: 1) automated content methods that allow to estimate the location of actors in policy space (and the differences between these methods and the human-coded ones); 2) automated content methods that allow to organize texts into a set of pre-defined categories (with a particular emphasis on social media texts); 3) automated content methods that allow to discover new ways of organizing texts into a set of unknown categories. Lab sessions are a crucial part of the course: they are offered for “hands-on” experiences to learn the techniques discussed during the morning class. An elementary knowledge of R, plus a curiosity towards applied statistics , are good prerequisites for the lab sessions. All the datasets and replication files of 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 textual analysis methods
2.Afternoon class (Lab): How to analyze texts with the Quanteda package
 
3.Morning class (Theory): From words to positions (Wordscores)
4.Afternoon class (Lab): How to implement the Wordscores algorithm using the Quanteda package
 
5.Morning class (Theory): From words to positions (Wordfish)
6.Afternoon class (Lab): How to implement the Wordfish algorithm using the Quanteda package
 
7.Morning class (Theory): From Manifestoes to positions: The Comparative Manifesto Project
8.Afternoon class (Lab): How to extract party positions from party Manifestoes
 
9.Morning class (Theory): From words to issues: The structural topic Model
10. Afternoon class (Lab): How to implement the stm package
 
11. Morning class (Lab): How to retrieve information from social media (part 1)
12. Afternoon class (Lab): How to retrieve information from social media (part 2)
 
13.Morning class (Theory): Dictionaries and Supervised classification methods
14.Afternoon class (Lab): How to extract information from social media (part 1)
 
15.Morning class (Theory): Supervised aggregated classification methods
16.Afternoon class (Lab): How to extract information from social media (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
Martin, Lanny W., and Georg Vanberg. 2008. A robust transformation procedure for interpreting political text. Political Analysis, 16: 93-100
Laver, Michael, and Kenneth Benoit. 2008. Compared to What? A Comment on “A Robust Transformation Procedure for Interpreting Political Text”, Political Analysis, 16: 101-111

Classes 5 & 6: 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 (with Airo Hino & Atsushi Osaka), Government and Opposition

Classes 7 & 8: Lowe, W., Benoit, K., Mikhaylov, S., & Laver, M. (2011). Scaling Policy Preferences From Coded Political Texts. Legislative Studies Quarterly, 26(1), 123-155.
Benoit, K., M. Laver, & S. Mikhaylov (2009). Treating Words as Data with Error: Uncertainty in Text Statements of Policy Positions. American Journal of Political Science, 53(2), 495-513
Huber, John and Matthew Gabel. (2000). Putting Parties in their Place: Inferring Party Left-Right Ideological Positions from Manifestos Data. American Journal of Political Science, 44 (1), 94-103

Classes 9 & 10: 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
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

Classes 13 & 14: 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, Andrea Ceron, and Stefano M. Iacus (2017). Politics and Big Data: Nowcasting and Forecasting Elections with Social Media. London: Routledge, 2017, Chapter 2

Classes 15 & 16: 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
Curini Luigi, Andrea Ceron, and Stefano M. Iacus (2018). ISIS at its apogee: the Arabic discourse on Twitter and what we can learn from that about ISIS support and Foreign Fighters", Sage Open
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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