Text mining in economics and health economics using Stata

Within the more relevant data science topics, text mining is an important and active research area that offers various ways to extract information and insights from text data. Its continued use and improvement could drive innovation in several areas and improve our ability to interpret, evaluate, and utilize the vast amounts of unstructured text produced in the digital age. Extracting insightful information from text data through text mining in healthcare and business holds great promise. Text mining in business can provide insightful information by analyzing large amounts of text data, including research papers, news, and Fnancial reports. It can help analyze market sentiment, identify emerging trends, and more accurately predict economic indicators by economists. For example, economists can Fnd terms or phrases that reQect investment behavior and sentiment changes by applying text-mining methods to Fnancial news. Text mining can provide essential insights into health economics by examining various textual data, including patient surveys, clinical trials, medical records, and health policy. Researchers and policymakers can use it to understand healthcare utilization patterns better, identify the variables that inQuence patient outcomes, and evaluate the effectiveness of different healthcare treatments. Text mining can examine electronic health data and identify trends in disease incidence, treatment effectiveness, and healthcare utilization. In this presentation, I will illustrate the instruments currently available in Stata to facilitate several text- mining methods.