Evans, James
Text as Data represents a major advance for teaching text analysis in the social sciences, digital humanities and data science by providing an integrated framework for how to conceptualize and deploy natural language processing techniques to enrich descriptive and causal analyses of social life in and from text. Here I review achievements of the book and highlight complementary paths not taken, including discussion of recent computational techniques like transformers, which have come to dominate automated language understanding and are just beginning to find their way into the careful research designs showcased in the book. These new methods not only highlight text as a signal from society, but textual models as simulations of society, which could fuel future advances in causal inference and experimentation. Text as Data's focus on textual discovery, measurement and inference points us toward this new frontier, cautioning us not to ignore, but build upon social scientific interpretation and theory.