Chris Bail
Duke University


This is the first in a series of tutorials I’ve created about collected data from web-based sources such as Facebook or Twitter and analyzing such data using a range of new techniques for automated text analysis. Before we proceed to the technical aspects of these techniques, I want to give you some sense of where the came from

A Brief History of Text as Data

The field of automated text analysis has been around for more than half a century, yet it has evolved very rapidly in recent years because of recent advances in the field of natural language processing. Perhaps the first person to propose the idea of quantifying patterns in text was Harold Laswell, who famously used this approach to study WWI propoganda. “We may classify references into categories,” wrote Laswell in 1938, " according to the understanding which prevails among those who are accustomed to the symbols. References used in interviews may be quantified by counting the number of references which fall into each category during a selected period of time (or per thousand words uttered)." Lasswell was ahead of his time. In 1935- and at the age of 21-Laswell was developing methods that tracked the association between word utterances and physiological reactions (e.g. pulse rate, electrical conductivity of the skin, and blood pressure).

Harold Laswell, Pioneer of Quantitative Text Analysis

But Laswell was but the first in a long line of pioneers in the field of quantitative text analysis that hailed from many different fields— from sociology to computer sience and linguistics. The table below provides an overview of some of the major milestones in the field.

Timeline of Quantitative Text Analysis

Time Activity
1934 Laswell Produces first Key-Word Count
1934 Vygotsky Produces first Quantitative Narrative Analaysis
1950 Gottschalk Uses Content Analysis to Track Freudian Themes
1950 Turing Applies AI to text
1952 Bereleson Publishes First Textbook on Content Analysis
1954 First Automatic Translation of Text (Georgetown Experiment)
1963 Msteller and Wallace analyze Federalist Papers
1965 Tomashevsky Further Formalizes Quantitative Narrative Analysis
1966 Stone and Bales use mainframe computer to measure psychometric properties of text at RAND
1980 Decline of Chomskyean Formalism/NLP is Born
1980 Machine Learning is Applied to NLP
1981 Weintraub counts parts of speech
1985 Schrodt Introduces Automated Event Coding
1986 Pennebaker develops LIWC
1988 First Latent Semantic Analysis Patent
1989 Franzosi brings Quantitative Narrative Analysis to Social Science
1998 Mohr’s Quantitative Analysis of Culture
1999 Bearman et al. apply Network Methods to Narratives
2001 Blei et al. develop LDA
2002 MALLET created
2005 Quinn et al. use analyze political speeches using topic models
2009 New Directions in Text Conference
2010 King/Hopkins Bring Supervised Learning into mainstream
2010 Tools for Text Workshop at Washington

What I find particularly remarkable about this (probably incomplete) timeline is that it covers scholars in at least seven different fields. Though social scientists made some of the earliest contributions to the field, computer scientists and linguists have exerted considerable influence in recent decades. The intellectual diversification of the field also coincided with the tremendous outgrowth of text-based data via the internet and other sources.

For some other great introductions to the field of text as data, see:

Justin Grimmer & Brandon Stewart. Text as Data: The Promises and Pitfalls of Automated Content Analysis, Political Analysis.

James Evans & Pedro Aceves. Machine Translation: Mining Text for Social Theory. Annual Review of Sociology.