Natural Language Processing for Policymaking

TL;DR

This chapter reviews how natural language processing techniques can be applied to policymaking, covering methods, applications, and ethical considerations in computational social science.

Contribution

It provides a comprehensive overview of NLP methods and their applications specifically tailored for policymaking and highlights associated limitations and ethical issues.

Findings

NLP methods enable data collection for evidence-based policies

They assist in interpreting political decisions and policy communication

Potential ethical concerns include bias and privacy issues

Abstract

Language is the medium for many political activities, from campaigns to news reports. Natural language processing (NLP) uses computational tools to parse text into key information that is needed for policymaking. In this chapter, we introduce common methods of NLP, including text classification, topic modeling, event extraction, and text scaling. We then overview how these methods can be used for policymaking through four major applications including data collection for evidence-based policymaking, interpretation of political decisions, policy communication, and investigation of policy effects. Finally, we highlight some potential limitations and ethical concerns when using NLP for policymaking. This text is from Chapter 7 (pages 141-162) of the Handbook of Computational Social Science for Policy (2023). Open Access on Springer: https://doi.org/10.1007/978-3-031-16624-2