Exploring Socioeconomic Challenges Using Latent Dirichlet Allocation and Text Mining: Convergence Points Between World Bank and The International Monetary Fund Reports

Socioeconomic challenges necessitate accurate trend prediction and in-depth analysis of their implications. This study applies Latent Dirichlet Allocation (LDA) topic modeling to examine recent reports from the World Bank and the International Monetary Fund (IMF) to uncover Algeria’s most pressing socioeconomic issues. By employing R software, structured data were extracted from unstructured textual content, utilizing Word Cloud visualization, "beta" frequencies for topic construction, and "gamma" proportions for topic relevance. The analysis identified key terms such as "fiscal," "hydrocarbon," "GDP," "debt," and "budget," indicating considerable thematic overlaps between the two institutions, despite nuanced differences in focus. Both organizations emphasize concerns related to fiscal sustainability and Algeria’s continued reliance on hydrocarbons, highlighting the country’s persistent macroeconomic vulnerabilities and the need for diversified economic strategies. Additionally, this research introduces an innovative conceptual network that graphically maps thematic interconnections, providing insights into the structural composition of institutional discourse. This approach facilitates a comparative assessment of economic narratives, helping policymakers discern areas of convergence and divergence between institutional perspectives. The study underscores the effectiveness of structured text-mining methodologies in analyzing economic discourse and institutional viewpoints, demonstrating their value in capturing complex economic themes. By systematically identifying thematic coherence, this research contributes to economic policy formulation and financial stability in resource-dependent economies. The findings offer valuable insights for policymakers and analysts, enhancing strategic decision-making through empirical evidence. This study highlights the role of data-driven analysis in addressing macroeconomic vulnerabilities and fostering sustainable economic development.