SUE.txt generates a text-based post-earnings-announcement drift (PEAD.txt) larger than the classic PEAD and can be used to create a profitable trading strategy. Leveraging the prediction model underlying SUE.txt, we propose new tools to study the news content of text: paragraph-level SUE.txt and paragraph classification scheme based on the business curriculum. With these tools, we document many asymmetries in the distribution of news across content types, demonstrating that earnings calls contain a wide range of news about firms and their environment.