<scp>Relatio</scp>: Text Semantics Capture Political and Economic Narratives

Ash, Elliott; Gauthier, Germain; Widmer, Philine

doi:10.1017/pan.2023.8

Cited by 8 publications

(4 citation statements)

References 44 publications

(41 reference statements)

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“…Hoberg & Phillips (2016) use a clustering method applied to product descriptions to construct industry categories. Ash et al (2023) apply clustering to embeddings of entity phrases, which works to dimension-reduce the set of entities mentioned in a corpus and interpretably identify connections among them.…”

Section: Problem I: Measuring Document Similaritymentioning

confidence: 99%

“…These types of actions and relationships and their directionality-that is, who does what to whom-are key to narratives and to human expression. Ash et al (2023) show how to use linguistic annotations for agents (the actors) and patients (the targets of actions) to construct and quantify such directed connections. In their application to US congressional speeches, the resulting micro narratives can be fit together in a network to represent distinctive partisan worldviews of US legislators.…”

Section: Problem Iii: How Concepts Are Relatedmentioning

confidence: 99%

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Text Algorithms in Economics

Ash

Hansen

2023

Annual Review of Economics

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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. Expected final online publication date for the Annual Review of Economics, Volume 15 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Problem I: Measuring Document Similaritymentioning

confidence: 99%

Section: Problem Iii: How Concepts Are Relatedmentioning

confidence: 99%

Text Algorithms in Economics

Ash

Hansen

2023

Annual Review of Economics

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“…This strategy, even if it succeeds in uncovering latent patterns, remains suboptimal and constrained (Grimmer and Stewart, 2013). It overlooks the semantic features stemming from documents (Ash et al, 2021), crucial to understanding narrative signals authors try to send throughout their texts.…”

Section: Introductionmentioning

confidence: 99%

5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

2023

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The development of the Internet and Big Data information sources is driving new and increasingly interdisciplinary research methods in economics and the social sciences. The 5th International Conference on Advanced Research Methods and Analytics (CARMA) is an excellent forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.

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Section: Introductionmentioning

confidence: 99%

Estimating Policy Uncertainty Within Monetary Policy Debates

Schütze,

Diaf

2023

5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

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Studying policy uncertainty contained in collections of documents has been a major task for political researchers and economists, who aim at measuring this degree exclusively with wordlists and topic models to feed further econometric inferences or test hypotheses. Such bag-of-word applications constrain the analysis and cannot render a clear picture of uncertainty drivers and their persistence, even if semi-supervised strategies may offer coherent improvements at the topic level. This work proposes a semantic search strategy, using Top2vec, to identify sources of uncertainty, at the debate level, and uncover coherent topics whose representations will be used to get uncertainty prevalence within each debate. Unlike aggregate-level measurements, this strategy is suited to study per speaker contributions at central banks, where uncertainty is regarded as a forward guidance tool and a key strategy when devising monetary policy actions. Applied to FOMC transcripts (1994-2016), the resulting semantic space yields non-overlapping topic vectors indicating a dominance of economic discussions in uncertainty formation within committee meetings, while risks concerns are bounded to financial markets and investments using an investor jargon. Moreover, results demonstrate the importance of experts' contributions in steering the economic debate, hence coloring uncertainty with words not found in traditional uncertainty wordlists and diffusing a significant persistence to uncertainty prevalence during debates that exhibits fractal patterns.

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Relatio: Text Semantics Capture Political and Economic Narratives

Cited by 8 publications

References 44 publications

Text Algorithms in Economics

Text Algorithms in Economics

5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

Estimating Policy Uncertainty Within Monetary Policy Debates

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