<p>Business topics are prominent in the news. The non-stop production of news, sometimes in real time, provides a potentially valuable source of data for understanding the business environment of regions or localities covered in the news. This study examines a multistage algorithmic framework to produce economic profiles from a corpus of news headlines. The methodology integrates topic modeling, named entity recognition, TF-IDF refinements, and network analysis in an algorithmic pipeline to identify key economic information. The algorithmic framework is structured around key natural language processing tools and techniques. It provides for processing headlines, assigning them to different topics, identifying business-centric topics, extracting relevant entities, analyzing relationships between business-centric topics, extracting relevant entities, and analyzing relationships between the business topic and other topics. The extracted information can be synthesized into an economic profile. This research is a domain-biased version of a system that has been used to automatically extract social problems from a corpus of 148,000 news headlines. The framework was applied to 1,280 news headlines to demonstrate its functionality in parts.</p>