lse-my459.github.io

LSE

MY459 - Quantitative Text Analysis

Lent Term 2019

Instructor

Teaching Assistant

Course Information

No lectures or classes will take place during School Reading Week 6.

Week Topic
1 Overview and Fundamentals
2 Descriptive Statistical Methods for Text Analysis
3 Automated Dictionary Methods
4 Machine Learning for Texts
5 Supervised Scaling Models for Texts
6 Reading Week
7 Unsupervised Models for Scaling Texts
8 Similarity and Clustering Methods
9 Topic models
10 Word embeddings
11 Working with Social Media

Course Description

The course surveys methods for systematically extracting quantitative information from political text for social scientific purposes, starting with classical content analysis and dictionary-based methods, to classification methods, and state-of-the-art scaling methods and topic models for estimating quantities from text using statistical techniques. The course lays a theoretical foundation for text analysis but mainly takes a very practical and applied approach, so that students learn how to apply these methods in actual research. The common focus across all methods is that they can be reduced to a three-step process: first, identifying texts and units of texts for analysis; second, extracting from the texts quantitatively measured features—such as coded content categories, word counts, word types, dictionary counts, or parts of speech—and converting these into a quantitative matrix; and third, using quantitative or statistical methods to analyse this matrix in order to generate inferences about the texts or their authors. The course systematically covers these methods in a logical progression, with a practical, hands-on approach where each technique will be applied using appropriate software to real texts.

Objectives

The course is also designed to cover many fundamental issues in quantitative text analysis such as inter-coder agreement, reliability, validation, accuracy, and precision. It focuses on methods of converting texts into quantitative matrixes of features, and then analysing those features using statistical methods. The course briefly covers the qualitative technique of human coding and annotation but only for the purposes of creating a validation set for automated approaches. These automated approaches include dictionary construction and application, classification and machine learning, scaling models, and topic models. For each topic, we will systematically cover published applications and examples of these methods, from a variety of disciplinary and applied fields but focusing on political science. Lessons will consist of a mixture of theoretical grounding in content analysis approaches and techniques, with hands on analysis of real texts using content analytic and statistical software.

Prerequisites

Students must have completed Applied Regression Analysis (MY452) or equivalent.

Students in this course will strongly benefit from prior experience with the R statistical package. All methods will be implemented in R, using primarily the R package quanteda, available from CRAN.

Assessment

Summative Assignments

Five lab sessions on the indicated weeks will consist of supervised problem sets. After each lab, assignments will be posted. These will involve computer exercises applied to texts supplied by the instructor. These will be submitted via GitHub Classroom prior to the next lecture, and will be marked to provide 60% of the course grade.

Summative Project

A final project of 3,000 words (5,000 words for MY559 students) will be due at the beginning of ST (on May 3rd at 5pm), and form 40% of the course grade. This will be an original analysis of texts using some of the methods covered in class, and may focus on replicating or extending a published work. Additional guidelines are available here.

Assessment criteria

Assignments will be marked using the following criteria:

Some of the assignemnts will involve shorter questions, to which the answers can be relatively unambiguously coded as (fully or partially) correct or incorrect. In the marking, these questions may be further broken down into smaller steps and marked step by step. The final mark is then a function of the proportion of parts of the questions which have been answered correctly. In such marking, the principle of partial credit is observed as far as feasible. This means that an answer to a part of a question will be treated as correct when it is correct conditional on answers to other parts of the question, even if those other parts have been answered incorrectly.

There is no really good single textbook that exists to cover computerized or quantitative text analysis. While not ideally fitting our core purpose, Krippendorf’s classic Content Analysis — just updated — is a good primer for manual methods of content analysis and coverage of some of the same fundamentals faced in quantitative text analysis.

Other readings will consist of articles and book excerpts, as listed below, which will either be made available via Moodle or through the links below.

Cheat Sheets

Cheat sheets contain useful code examples to get you started. Please refer to these materials before you book office hours!

Quanteda
Regular Expressions
Glob

Additional resources

This class is supported by DataCamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises. Take over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalised feedback on every exercise.

Credits

A large proportion of the materials were adapted from content developed by Prof. Kenneth Benoit for previous versions of this course. Some of the assignments were developed by Christian Mueller and Akitaka Matsuo.

Schedule

Week 1. Overview and fundamentals

This session will cover fundamentals, including the continuum from traditional (non-computer assisted) content analysis to fully automated quantitative text analysis. We will cover the conceptual foundations of content analysis and quantitative content analysis, discuss the objectives, the approach to knowledge, and the particular view of texts when performing quantitative analysis.

Reading:

Further Reading:

Week 2: Descriptive statistical methods for text analysis

Here we focus on quantitative methods for describing texts, focusing on summary measures that highlight particular characteristics of documents and allowing these to be compared. We will also discuss issues including where to obtain textual data; formatting and working with text files; indexing and meta-data; units of analysis; and definitions of features and measures commonly extracted from texts, including stemming, and stop-words.

Reading:

Further Reading:

Week 3: Automated dictionary methods

Automatic dictionary-based methods involve association of pre-defined word lists with particular quantitative values assigned by the researcher for some characteristic of interest. This topic covers the design model behind dictionary construction, including guidelines for testing and refining dictionaries. Hand-on work will cover commonly used dictionaries such as LIWC, RID, and the Harvard IV-4, with applications. We will also review a variety of text pre-processing issues and textual data concepts such as word types, tokens, and equivalencies, including word stemming and trimming of words based on term and/or document frequency.

Reading:

Further Reading:

Week 4: Machine Learning for Texts

Classification methods permit the automatic classification of texts in a test set following machine learning from a training set. We will introduce machine learning methods for classifying documents, including one of the most popular classifiers, the Naive Bayes model. The topic also introduces validation and reporting methods for classifiers and discusses where these methods are applicable.

Reading:

Further Reading:

Week 5: Supervised Scaling Models for Texts

Building on the Naive Bayes classifier, we introduce the “Wordscores” method of Laver, Benoit and Garry (2003) for scaling latent traits, and show the link between classification and scaling.

Reading:

Further Reading:

Week 7: Unsupervised Models for Scaling Texts

This session continues text scaling using unsupervised scaling methods, based on parametric approaches modelling features as Poisson distributed (Wordfish and Wordshoal) or non-parametric approaches such as correspodence analysis.

Reading:

Further Reading:

Week 8: Similarity and clustering methods

Vector representations of documents, measuring distance and similarity, hierarchical and k-means clustering. This topic also revisits feature selection and weighting methods, especially tf-idf.

Reading:

Further Reading:

Week 9: Topic models

This session will teach how to automatically classify documents into unknown categories using topic models. We will learn how to run the parametric Latent Dirichlet Allocation (LDA) model and the Structural Topic Model (STM), which allows researchers to use covariates to learn about the prevalence and content of topics.

Reading:

Further Reading:

Week 10: Word embeddings

This week will cover vector representation of words as an alternative way to construct document-feature matrices, with particular attention to word embeddings as a popular type of vector space representation.

Reading:

Further Reading:

Week 11: Working with Social Media

Social media such as micro-blogging site Twitter provide a wealth of spontaneous, distributed, real-time text that can be used to analyze almost any topic. We introduce the growing literature applying text analysis techniques to this form of data, with examples for measuring sentiment, networks, and locational information.

Reading:

Further Reading:

References

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Beil, F, M Ester and X Xu. 2002. Frequent term-based text clustering. In Eighth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 436–442.

Benoit, K. and M. Laver. 2008. “Compared to What? A Comment on ‘A Robust Transformation Procedure for Interpreting Political Text’ by Martin and Vanberg.” Political Analysis 16(1):101–111. doi: 10.1093/pan/mpm020.

Benoit, Kenneth and Paul Nulty. 2013. “Classification Methods for Scaling Latent Political Traits.” Presented at the Annual Meeting of the Midwest Political Science Association, April 11–14, Chicago.

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