Work

Natural Language Processing and Innovation Research

Year: 2026

Type: review

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.

Source: Annual Review of Economics

DOI: 10.1146/annurev-economics-072225-113623

Authors: Antonin Bergeaud, Adam Jaffe, Dimitris Papanikolaou

Institutions: Centre for Economic Policy Research, HEC Paris, National Bureau of Economic Research, Brandeis University, Northwestern University

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W7135240961

FWCI:

Cites: 43

Cited by:

Related to: