The News Sentiment application enhances the discoverability of news content through our flagship platform, Capital IQ Pro. We processed news articles for 10,000+ public companies through entity recognition, along with a series of proprietary financial sentiment models to assess whether the news was positive or negative, as well as its significance and relevance to the company. We built a database containing over 1.5 million signals and operationalized the end-to-end ETL as a daily Workflow on Databricks. The development process included model training and selection. We utilized training data from our internal financial analysts to train Google’s T5-Flan to create our proprietary sentiment model and two additional models. Our models are deployed on Databricks Model-Serving as serverless endpoints that can be queried on-demand. The last phase of the project was to develop a UI, in which we utilized Databricks serverless SQL warehouses to surface this data in real-time.