Quantitative text analysis|public policy|social science|machine learning

Growth of text data in recent years, and the development of a set of sophisticated tools for analysing that data, offers important opportunities for social...

Quantitative Text Analysis for Social Science (PUBL0099)

Growth of text data in recent years, and the development of a set of sophisticated tools for analysing that data, offers important opportunities for social scientists to study questions that were previously amenable to only qualitative analyses.

This module will allow students to take advantage of these opportunities by providing them with an understanding of, and ability to apply, tools of quantitative text analysis to answer important questions in the fields of social science and public policy.

The module is centred around three core components of a typical quantitative text analysis project.
First, students will learn to collect text data at scale. Students will study how to scrape text data from the web, and how to use methods such as Optimal Character Recognition to extract digitized text data from printed physical documents.
Second, students will learn the various ways in which written texts can be converted into data to use in quantitative analyses. This includes extracting features from the texts, such as coded categories, word counts, word types, dictionary counts, parts of speech, and so on.
Third, the module covers a range of methods for systematically extracting quantitative information from digitized texts, including traditional approaches such as content analysis and dictionary-based methods; supervised learning approaches for text classification methods and text scaling; and also recent advances in semi-supervised and unsupervised learning for texts, such as topic models and word-embedding models.

The module has a strongly applied focus, with students learning to collect, manipulate, and analyse data themselves. The module covers a wide range of examples of how these methods are used in the social sciences, in business, and in government.

 