%L discovery10171178
%I Annual Reviews
%D 2023
%P 659-688
%O © 2023 by the Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
%T Text Algorithms in Economics
%X This article provides an overview of the methods used for algorithmic text analysis in economics, with a focus on three key contributions. First, we introduce methods for representing documents as high-dimensional count vectors over vocabulary terms, for representing words as vectors, and for representing word sequences as embedding vectors. Second, we define four core empirical tasks that encompass most text-as-data research in economics and enumerate the various approaches that have been taken so far to accomplish these tasks. Finally, we flag limitations in the current literature, with a focus on the challenge of validating algorithmic output.
%K text as data, topic models, word embeddings, large language models, transformer models, JEL C18, JEL C45, JEL C55
%A Stephen Hansen
%V 15
%J Annual Review of Economics