Forward or Backward Looking? The Economic Discourse and the Observed Reality

Lüdering, Jochen; Winker, Peter

doi:10.1515/jbnst-2015-1026

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…For example, the 'history of humanities computing' can be traced back to Father Roberto Busa, who indexed the work of Thomas Aquinas in the late 1940s, see Hockey (2004) and Jockers (2013). For a brief description of the recent development of topic models, see Lüdering and Winker (2016). Winker 2016, p. 493) is Latent Dirichlet Allocation (LDA), which was introduced by David Blei et al (2003).…”

Section: The Principles Of Topic Modelingmentioning

confidence: 99%

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Economic history goes digital: topic modeling the Journal of Economic History

Wehrheim

2018

Cliometrica

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Digitization and computer science have established a whole new set of methods to analyze large collections of texts. One of these methods is particularly promising for economic historians: topic models, statistical algorithms that automatically infer themes from large collections of texts. In this article, I present an introduction to topic modeling and give a very first review on the research using topic models. I illustrate their capacity by applying them on 2.675 articles published in the Journal of Economic History between 1941 and 2016. This contributes to traditional research on the JEH and to current research on the cliometric revolution.JEL-Classification: A12, C18, N01 as well as the participants in the research seminar in economic and social history at University of Regensburg for invaluable advice. This paper was presented in the lecture series on digital humanities at University of Regensburg.

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Section: The Principles Of Topic Modelingmentioning

confidence: 99%

“…Studying also the FOMC, they investigate the effects of central bank communication on macroeconomic and financial variables. Another example of the integration of topic models into economic analysis is given by Jochen Lüdering and Peter Winker (2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Economic history goes digital: topic modeling the Journal of Economic History

Wehrheim

2018

Cliometrica

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“…Text mining algorithms can be used to extract knowledge from large document collections and turn them into valuable economic information (6)(7)(8)(9)(10). As a result, text mining became one of the most promising approaches in economic analysis to provide novel tools and insights to economists.…”

mentioning

confidence: 99%

Predicting Innovative Firms Using Web Mining and Deep Learning

Kinne

Lenz

2019

SSRN Journal

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Innovation is considered as a main driver of economic growth. Promoting the development of innovation through STI (science, technology and innovation) policies requires accurate indicators of innovation. Traditional indicators often lack coverage, granularity as well as timeliness and involve high data collection costs, especially when conducted at a large scale. In this paper, we propose a novel approach on how to create firm-level innovation indicators at the scale of millions of firms. We use traditional firm-level innovation indicators from the questionnaire-based Community Innovation Survey (CIS) survey to train an artificial neural network classification model on labelled (innovative/non-innovative) web texts of surveyed firms. Subsequently, we apply this classification model to the web texts of hundreds of thousands of firms in Germany to predict their innovation status. Our results show that this approach produces credible predictions and has the potential to be a valuable and highly cost-efficient addition to the existing set of innovation indicators, especially due to its coverage and regional granularity. The predicted firm-level probabilities can also directly be interpreted as a continuous measure of innovativeness, opening up additional advantages over traditional binary innovation indicators.

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“…Scientific discourse modeling is also within the scope of topic models. For example, [18] identified topics in the Journal of Economics and Statistics to study whether or not the scientific discussion of topics correlates with the actual development of economic key indicators. [19] examined how central bank communications affect real economic variables using topic modeling, and [20] employed LDA to model the topics in the Journal of Economic History between 1941 and 2016.…”

Section: Fundingmentioning

confidence: 99%

Measuring the diffusion of innovations with paragraph vector topic models

Lenz

Winker

2020

PLoS ONE

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Measuring the diffusion of innovations from textual data sources besides patent data has not been studied extensively. However, early and accurate indicators of innovation and the recognition of trends in innovation are mandatory to successfully promote economic growth through technological progress via evidence-based policy making. In this study, we propose Paragraph Vector Topic Model (PVTM) and apply it to technology-related news articles to analyze innovation-related topics over time and gain insights regarding their diffusion process. PVTM represents documents in a semantic space, which has been shown to capture latent variables of the underlying documents, e.g., the latent topics. Clusters of documents in the semantic space can then be interpreted and transformed into meaningful topics by means of Gaussian mixture modeling. In using PVTM, we identify innovation-related topics from 170, 000 technology news articles published over a span of 20 years and gather insights about their diffusion state by measuring the topic importance in the corpus over time. Our results suggest that PVTM is a credible alternative to widely used topic models for the discovery of latent topics in (technology-related) news articles. An examination of three exemplary topics shows that innovation diffusion could be assessed using topic importance measures derived from PVTM. Thereby, we find that PVTM diffusion indicators for certain topics are Granger causal to Google Trend indices with matching search terms.

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Forward or Backward Looking? The Economic Discourse and the Observed Reality

Cited by 16 publications

References 20 publications

Economic history goes digital: topic modeling the Journal of Economic History

Economic history goes digital: topic modeling the Journal of Economic History

Predicting Innovative Firms Using Web Mining and Deep Learning

Measuring the diffusion of innovations with paragraph vector topic models

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