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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: (Part 1): From words to issues: structural topic models; (Part 2): Dictionary models
Reference texts (1, 2):
2/10/21 Lab class: How to implement a Structural Topic Model and dictionary models (scripts: a) packages to install; b) Lab 5 script: STM; c) Lab 5 dictionaries; d) Lab 5 dictionaries and Twitter; dataset: a) NyT; b) data for topical content analysis)
Fifth Assignment (due: 15 February 2021)
Sixth Lecture
2/15/21 Theory class: (Part 1): From words to issues: semi-supervised classification models; (Part 2): An introduction to supervised classification models
Reference texts (1, 2, 3, 4):
2/16/21 Lab class: How to implement a semi-supervised classification model (scripts: a) packages to install; b) Lab 6 script: Newsmap; c) Lab 6 script: KeyATM)
Sixth Assignment (due: 22 February 2021)
Seventh Lecture
2/22/21 Theory class: From words to issues: supervised classification models
Reference text (1):
2/23/21 Lab class: How to implement supervised classification models (scripts: a) packages to install; b) Lab 7 script; datasets: a) movie reviews training-set; b) movie reviews test-set; c) Nationality)
Seventh Assignment (due: 1 March 2021) (datasets for Assignment 7: a) UK training set; b) UK test set)
Eighth Lecture
3/1/21 Theory class: How to validate the results you get from machine learning algorithms
Reference text (1, 2, 3):
3/2/21 Lab class: How to apply k-fold cross validation (scripts: a) package to install; b) Lab 8 script (part A); c) Lab 8 script (part B); d) Lab 8 script (part C)
Eigth Assignment (due: 8 March 2021)
Ninth Lecture
3/8/21 Theory class: (Part 1): Some further algorithms for ML; (Part 2): The importance of the training set
Reference texts (1; 2):
Ninth Assignment (due: 15 March 2021)
Tenth Lecture
3/15/21 Theory class: (Part 1): Proportional algorithms; (Part 2): An introduction to word embeddings
Reference texts (1, 2, 3):
3/16/21 Lab class: How to implement a proportional algorithm and a word-embedding procedure. (scripts: a) packages to install; b) Lab 10 script (part 1); c) Lab 10 script (part 2); dataset: a) email spam training-set; b) email spam test-set; c) pre-trained WE)
Tenth Assignment (due: 22 March 2021)
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-Oliver, 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-Oliver, 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. 2020. 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: (Part 1): From words to issues: structural topic models; (Part 2): Dictionary models
Reference texts (1, 2):
- 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
- 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
2/10/21 Lab class: How to implement a Structural Topic Model and dictionary models (scripts: a) packages to install; b) Lab 5 script: STM; c) Lab 5 dictionaries; d) Lab 5 dictionaries and Twitter; dataset: a) NyT; b) data for topical content analysis)
Fifth Assignment (due: 15 February 2021)
Sixth Lecture
2/15/21 Theory class: (Part 1): From words to issues: semi-supervised classification models; (Part 2): An introduction to supervised classification models
Reference texts (1, 2, 3, 4):
- Kohei Watanabe and Yuan Zhou (2020) Theory-Driven Analysis of Large Corpora: Semisupervised Topic Classification of the UN Speeches. Social Science Computer Review, DOI: 10.1177/0894439320907027
- Shusei Eshima, Kosuke Imai, and Tomoya Sasaki (2020). Keyword Assisted Topic Models, arXiv:2004.05964v1
- 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, and Robert Fahey. 2020. Sentiment Analysis. In: Luigi Curini and Robert Franzese (eds.), Sage Handbook of Research Methods in Political Science and International Relations, London: Sage, chapter 29
2/16/21 Lab class: How to implement a semi-supervised classification model (scripts: a) packages to install; b) Lab 6 script: Newsmap; c) Lab 6 script: KeyATM)
Sixth Assignment (due: 22 February 2021)
Seventh Lecture
2/22/21 Theory class: From words to issues: supervised classification models
Reference text (1):
- Olivella, Santiago, and Shoub Kelsey (2020). Machine Learning in Political Science: Supervised Learning Models. In Luigi Curini and Robert Franzese (eds.), SAGE Handbook of Research Methods is Political Science & International Relations, London, Sage, chapter 56
2/23/21 Lab class: How to implement supervised classification models (scripts: a) packages to install; b) Lab 7 script; datasets: a) movie reviews training-set; b) movie reviews test-set; c) Nationality)
Seventh Assignment (due: 1 March 2021) (datasets for Assignment 7: a) UK training set; b) UK test set)
Eighth Lecture
3/1/21 Theory class: How to validate the results you get from machine learning algorithms
Reference text (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
- Cranmer, Skyler J. and Desmarais, Bruce A. (2017) What Can We Learn from Predictive Modeling?, Political Analysis, 25: 145-166
- Curini, Luigi, and Robert Fahey. 2020. Sentiment Analysis. In: Luigi Curini and Robert Franzese (eds.), Sage Handbook of Research Methods in Political Science and International Relations, London: Sage, chapter 29
3/2/21 Lab class: How to apply k-fold cross validation (scripts: a) package to install; b) Lab 8 script (part A); c) Lab 8 script (part B); d) Lab 8 script (part C)
Eigth Assignment (due: 8 March 2021)
Ninth Lecture
3/8/21 Theory class: (Part 1): Some further algorithms for ML; (Part 2): The importance of the training set
Reference texts (1; 2):
- Olivella, Santiago, and Shoub Kelsey (2020). Machine Learning in Political Science: Supervised Learning Models. In Luigi Curini and Robert Franzese (eds.), SAGE Handbook of Research Methods is Political Science & International Relations, London, Sage, chapter 56
- Barberá, Pablo et al. (2020). Automated Text Classification of News Articles: A Practical Guide. Political Analysis, DOI: 10.1017/pan.2020
Ninth Assignment (due: 15 March 2021)
Tenth Lecture
3/15/21 Theory class: (Part 1): Proportional algorithms; (Part 2): An introduction to word embeddings
Reference texts (1, 2, 3):
- Ceron, Andrea, Curini, Luigi and Stefano M. Iacus Justin, and Stewart, Brandon M. (2016). iSA: a fast, scalable and accurate algorithm for sentiment analysis of social media content, Information Sciences, 367–368 (1), 105–124
- Rodriguez Pedro L. and Spirling Arthur (2021). Word Embeddings: What works, what doesn’t, and how to tell the difference for applied research, Journal of Politics, forthcoming
- Rudkowsky Elena, et al. (2018). More than Bags of Words: Sentiment Analysis with Word Embeddings. Communication Methods and Measures. 12:2-3, 140-157
3/16/21 Lab class: How to implement a proportional algorithm and a word-embedding procedure. (scripts: a) packages to install; b) Lab 10 script (part 1); c) Lab 10 script (part 2); dataset: a) email spam training-set; b) email spam test-set; c) pre-trained WE)
Tenth Assignment (due: 22 March 2021)