Authors: Justin Grimmer, Margaret E. Roberts and Brandon M. Stewart
From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world.
This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry.
Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted.
Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights.
Text as Data is organized around the core tasks in research projects using text — representation, discovery, measurement, prediction, and causal inference.