Text Algorithms in Economics

Elliott Ash and Stephen Hansen

Annual Review of Economics, 2023, vol. 15, issue 1, 659-688

Abstract: 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.

Keywords: text as data; topic models; word embeddings; large language models; transformer models (search for similar items in EconPapers)
JEL-codes: C18 C45 C55 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (14)

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DOI: 10.1146/annurev-economics-082222-074352

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