Over the past few decades, technology has been utilized to increase accessibility and efficiency of government processes. One area currently seeing rapid innovation is regulatory rulemaking, which is the process by which legislation passed by Congress is translated into specific actionable rules by federal agencies. This process has historically been inaccessible due to high information costs and minimal incentives to participate for the average individual or small organizations. Recent efforts by the General Services Administration has pushed this process online allowing anyone to track and comment on rules of interest. In this paper, I take advantage of parallel developments to download these large sets of comments and utilize new text analysis methods to extract trends, themes and patterns from two sets of rules. This work highlights the importance of using technology and specifically text analysis to improve the rulemaking process for both citizens and regulators. Part 2 of the paper gives a brief overview and history of the rulemaking and public comment process. Part 3 explores the current literature on computational analysis of regulatory comments. Part 4 describes the data collection process and specific steps taken to process the data. Part 5 describes the methods of text preprocessing, word frequency and topic model used to conduct the analysis. Part 6 reviews the results of the analyses and provides some interpretations. Part 7 discusses the results in the larger context of the literature and outlines steps for future research.