Text Mining and Hierarchical Clustering in Stata

This document is a technical report that outlines the application of text mining and hierarchical clustering techniques using Stata for real-time policy monitoring, forecasting, and literature mapping. It begins by addressing the growing challenge of unstructured textual data and presents two key applications: policy analysis and financial forecasting with sentiment analysis. The report details a framework that integrates Stata, Python, and R, aiming to assist researchers and policymakers. It discusses the text mining pipeline, including preprocessing steps, vectorization, and clustering methods. The document also includes case studies demonstrating the practical implementation of these techniques in economic text analysis and S&P 500 forecasting. The findings indicate the effectiveness of sentiment analysis in financial forecasting and the organization of large text corpora for policy insights. The report concludes with recommendations for future applications and the potential real-world impact of the methodologies presented.