Unveiling Policy Volatility and Its Economic Implications: A Text Mining Analysis of Government Narratives

Gaining insights into government policy volatility is crucial as it impacts economic growth, market stability, investment patterns, and consumer confidence (Bertoni, Colombo, and Quas 2015). Policy volatility, including frequent shifts, reversals, or unexpected changes introduces uncertainty, increases costs, and erodes stakeholder trust. Traditional methods of assessing policy impact primarily rely on historical economic data and official reports, which often lag behind real-time events and fail to capture immediate public sentiment (Apeti, Combes, and Minea 2024). This study explores Natural Language Processing (NLP) as a methodological approach for analyzing government policy volatility. It does so through the application of text mining techniques to official political communications, specifically presidential speeches. By employing Latent Dirichlet Allocation (LDA), the research identifies latent thematic structures within these texts, enabling the detection of policy shifts and trends over time. While previous studies have predominantly relied on quantitative indicators to examine policy volatility, this study introduces a novel approach by leveraging textual data. Presidential speeches, as key government narratives, provide insights into policy priorities and their evolution over time. The application of LDA helps to identify underlying themes in policy discourse, allowing researchers and policymakers to anticipate policy shifts, assess their implications, and create strategies for economic stability. By employing a text mining approach, this study aims to bridge research gap in policy analysis, particularly in countries where policy unpredictability poses economic challenges (Mitchell 1997). The findings will contribute to the literature on government policy volatility by offering a framework for analyzing textual data in political and economic research. Additionally, the study aligns with the broader objective of evidence-based policymaking by providing empirical insights into policy formulation and implementation. Using a developing country as a case study, this research enhances the understanding of policy volatility's role in economic growth and development, offering valuable implications for policymakers, researchers, and stakeholders.

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Publication Details

Journal
Journal of the Association for Information Systems
Published
2025-08-16
Primary Topic
scientometrics and bibliometrics research
Type
article
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article

Unveiling Policy Volatility and Its Economic Implications: A Text Mining Analysis of Government Narratives

Journal of the Association for Information Systems
scientometrics and bibliometrics research
article

Unveiling Policy Volatility and Its Economic Implications: A Text Mining Analysis of Government Narratives

article en

Abstract

Gaining insights into government policy volatility is crucial as it impacts economic growth, market stability, investment patterns, and consumer confidence (Bertoni, Colombo, and Quas 2015). Policy volatility, including frequent shifts, reversals, or unexpected changes introduces uncertainty, increases costs, and erodes stakeholder trust. Traditional methods of assessing policy impact primarily rely on historical economic data and official reports, which often lag behind real-time events and fail to capture immediate public sentiment (Apeti, Combes, and Minea 2024). This study explores Natural Language Processing (NLP) as a methodological approach for analyzing government policy volatility. It does so through the application of text mining techniques to official political communications, specifically presidential speeches. By employing Latent Dirichlet Allocation (LDA), the research identifies latent thematic structures within these texts, enabling the detection of policy shifts and trends over time. While previous studies have predominantly relied on quantitative indicators to examine policy volatility, this study introduces a novel approach by leveraging textual data. Presidential speeches, as key government narratives, provide insights into policy priorities and their evolution over time. The application of LDA helps to identify underlying themes in policy discourse, allowing researchers and policymakers to anticipate policy shifts, assess their implications, and create strategies for economic stability. By employing a text mining approach, this study aims to bridge research gap in policy analysis, particularly in countries where policy unpredictability poses economic challenges (Mitchell 1997). The findings will contribute to the literature on government policy volatility by offering a framework for analyzing textual data in political and economic research. Additionally, the study aligns with the broader objective of evidence-based policymaking by providing empirical insights into policy formulation and implementation. Using a developing country as a case study, this research enhances the understanding of policy volatility's role in economic growth and development, offering valuable implications for policymakers, researchers, and stakeholders.

Journal of the Association for Information Systems
Ghana Institute of Management and Public Administration (GH)
Partnerships for the goals
Openalex Percentile: Top 46%
scientometrics and bibliometrics research
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Unveiling Policy Volatility and Its Economic Implications: A Text Mining Analysis of Government Narratives · Journal of the Association for Information Systems (2025) | TGRS Research Map | TGRS