Understanding Citizens' Direct Policy Suggestions to the Federal Government: A Natural Language Processing and Topic Modeling Approach

Loni Hagen · Özlem Uzuner · Christopher Kotfila · Teresa M. Harrison · Dan LaManna

48 citations0/0 files storedHawaii International Conference on System Sciences (HICSS)

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Computational and Text Analysis Methods E-Government and Public Services Sentiment Analysis and Opinion Mining

Abstract

We report on our initial efforts to make sense of e-petitions as policy suggestions by using the NLP technique of "topic modeling" to identify the "topics" that emerge in e-petitions. Using a sample of petitions submitted to the Obama Administration's WtP petitioning system as a case study, we produced 30 emergent topics. 21 out of the 30 topics were initially coded as high-quality topics. Upon qualitative investigation, all but one of these 21 topics were determined to have a coherent theme. Our results imply that topic modeling has the potential to enable the interpretation of large quantities of citizen generated policy suggestions through a largely automated process, with potential application to research on e-participation and policy informatics.

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2013Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts 2011topicmodels : An R Package for Fitting Topic Models 2009A Method of Automated Nonparametric Content Analysis for Social Science 2019An Introduction to the Policy Process 2009How to Analyze Political Attention with Minimal Assumptions and Costs 2009A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases 2003Information and American democracy: technology in the evolution of political power 2003Information and American Democracy

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