TY  - JOUR
VL  - 15
PB  - Annual Reviews
A1  - Hansen, Stephen
Y1  - 2023/09//
N2  - 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.
AV  - public
JF  - Annual Review of Economics
EP  - 688
N1  - © 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/).
ID  - discovery10171178
KW  - text as data
KW  -  topic models
KW  -  word embeddings
KW  -  large language models
KW  -  transformer models
KW  -  JEL C18
KW  -  JEL C45
KW  -  JEL C55
TI  - Text Algorithms in Economics
SP  - 659
UR  - https://www.annualreviews.org/journal/economics
ER  -