Hansen, Stephen;
(2023)
Text Algorithms in Economics.
Annual Review of Economics
, 15
pp. 659-688.
10.1146/annurev-economics-082222-074352.
Preview |
Text
Hansen_Text Algorithms in Economics_VoR.pdf Download (1MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Text Algorithms in Economics |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1146/annurev-economics-082222-074352 |
| Publisher version: | https://www.annualreviews.org/journal/economics |
| Language: | English |
| Additional information: | © 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/). |
| Keywords: | text as data, topic models, word embeddings, large language models, transformer models, JEL C18, JEL C45, JEL C55 |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10171178 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
