Using Machine Learning Techniques and Algorithms for Predicting the Time Length of Publishing a Law (TLOPL) in the Domain of e-Parliament

The analysis of legislative data using machine learning has the potential to greatly enhance parliamentary policymaking. With a focus on estimating when laws and amendments will be published in Parliament, this literature review seeks to learn more about how machine learning can be used in legislative decision-making. However, more than a basic platform with integrated tools and software applications is required for the legislative workspace, particularly during the policy development stage, where many users, such as parliamentary actors and/or stakeholders, are frequently active. To find studies about machine learning application to parliamentary research, a thorough search of electronic databases was performed. To better understand how machine learning is being used in the legislative branch, we conducted a Systematic Literature Review (SLR) of 35 primary papers. The objective of this study is to examine the use of machine learning in legislative decision-making. In addition, we pointed out research needs and gaps and predicted developments in this area.