LSE MY459 Quantitative Text Analysis

LSE

Lecture materials for MY459 and MY360 - Quantitative Text Analysis

Please note that the information on this page is provisional until the start of the term.

Winter Term 2025

Instructors

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Course Information

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 Probabilistic Topic Models
10 Word Embedding Fundamentals
11 Neural Network Based Language Models

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 MY452 (for MY459) or ST107 (for MY360), or equivalent.

All methods will be implemented in R, often using the R package quanteda, available from CRAN.

We will assume all students have a strong working knowledge of R and sufficient experience using it for data analysis. See Moodle for more detail on how you can prepare.

Assessments

Quantitative text analysis encompases a very wide range of methods of varying degrees of complexity, so no single textbook can hope to cover all topics in the field. However, there are a few textbooks that provide nice coverage of many of the concepts and topics we will cover in this course:

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

Coding Cheat Sheets

Coding “cheat sheets” contain useful code examples to get you started. Please refer to these materials before you book office hours!

Credits

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

Provisional Schedule of Topics

The following course schedule is provisional and subject to revisions before the beginning of the Winter Term. Although we do not anticipate major changes in the following course content, some of the topics and readings may be modified or reorganised.

Important note: links to lecture slides and other course materials will be updated/added in advance of each week’s teaching.

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:

Seminar Materials: See the Moodle page for your course (links above).

Github Reference Materials: Click here

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:

Seminar Materials: See the Moodle page for your course (links above).

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 6: NO LECTURES OR SEMINARS (READING WEEK)

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:

Seminar Materials: See the Moodle page for your course (links above).

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: Probabilistic topic models

This session will discuss probabilistic topic models. We will learn how to run the 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:

Seminar Materials: See the Moodle page for your course (links above).

Week 10: Word embedding fundamentals

This week will discuss fundamentals of numerical vector representations for words.

Reading:

Further Reading:

Week 11: Neural network based language models

This week will give a high level overview of some current neural network based models for text that go beyond the bag-of-words assumption.

Before the lecture:

Further materials (optional!):

Seminar Materials: See the Moodle page for your course (links above).

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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