ARlegislation: An R Package of Arkansas Legislation Data and an Exploratory Use Case for Using Machine Learning to Identify Public Corruption

Author

• Nathan P. Chaney

Date of Award

8-17-2020

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Information Science

First Advisor

Xiaowei Xu

Abstract

This thesis describes the creation of a natural-language dataset from a corpus of legislation passed in the State of Arkansas between 2001 and 2019. The dataset also includes metadata about individual acts of legislation and the lawmakers who sponsored them. This thesis describes the creation of the dataset, including the transformation of raw textual input using various natural language processing techniques such as sentiment analysis and topic modeling. Finally, this thesis examines a use case for identifying corrupt lawmakers using machine learning tools trained on transformations of the dataset.

Recommended Citation

Chaney, Nathan P., "ARlegislation: An R Package of Arkansas Legislation Data and an Exploratory Use Case for Using Machine Learning to Identify Public Corruption" (2020). Theses and Dissertations. 951.
https://research.ualr.edu/etd/951