10.57647/j.amc.2025.090212

Text Mining Analysis of Agricultural Subsidy Studies: Identifying Patterns and Policy Insights

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  • Payam Panahian1,
  • Vali Borimnejad*1,
  • Davood Samari1,
  • Niv Nozari1,
  1. Department of Agricultural Economics and Management, Ka.C., Islamic Azad University, Karaj, Iran

Received: 2025-10-13

Revised: 2025-11-05

Accepted: 2025-11-11

Published in Issue 2025-12-30

Copyright (c) 2025 Payam Panahian, Vali Borimnejad, Davood Samari, Niv Nozari (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Panahian, P., Borimnejad, V., Samari, D., & Nozari, N. (2025). Text Mining Analysis of Agricultural Subsidy Studies: Identifying Patterns and Policy Insights. Agricultural Marketing and Commercialization, 9(2). https://doi.org/10.57647/j.amc.2025.090212
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Abstract

This paper conducts a text-mining analysis of scientific studies related to agricultural subsidies. The primary goal of this research is to identify hidden patterns and meaningful relationships within the textual data found in various articles on agricultural subsidies. 512 scientific papers from 2000 to 2025 were examined using advanced text mining methods and semantic modeling. This study employs word frequency analysis, topic modeling with the LDA algorithm, semantic clustering using K-Means, and sentiment analysis. The results revealed that the primary focus of the studies was on "subsidies," "agriculture," and "fertilizers." Additionally, the analyses indicated that agricultural subsidies significantly impact farmers' productivity and reduce economic inequalities. Furthermore, the findings highlight gaps in the attention given to environmental and social concepts such as "justice," "environment," "women," and "youth" in this field.

Keywords

  • Agricultural subsidies,
  • , text mining,,
  • topic modeling,
  • sentiment analysis,
  • research gaps
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