Abstract
Innovation is central to models in economics, strategy, management, and finance, yet it remains difficult to measure due to its intangible and knowledge-based nature. Recent advancements in natural language processing offer new methods to analyze textual artifacts, providing empirical insights into previously hard-to-measure aspects of innovation. This article provides an overview of the current applications of these methods in empirical innovation research, highlights their transformative potential for reshaping how researchers study and quantify innovation, and discusses the critical challenges that accompany their use.