Leveraging Machine Learning for Congressional Engagement

This technical report outlines how Fireside21 is utilizing machine learning and natural language processing to enhance the US Congress's ability to manage constituent communications. The report begins by detailing the challenges faced by Congress due to the increasing volume of messages from citizens, which has surged to over 10 million annually. It emphasizes the importance of technology in improving the responsiveness of congressional staff to these communications. The document describes the machine learning process, including problem statement identification, data collection, exploratory data analysis, and model building. It highlights the goal of creating an inbound message classification system that predicts responses with at least 80 percent accuracy. The report also discusses the iterative nature of the machine learning process and the significance of selecting appropriate data sources for model training. By implementing these technologies, Fireside21 aims to foster better relationships between Congress and citizens, ultimately enhancing democratic engagement.