mlsanityData to 7 Oct 2026

Natural Language Processing for Policymaking

arXiv 2302.03490 ↗First posted 7 Feb 2023cs.CL cs.AI cs.CYcited by 1

Not on the map. Why?

Abstract (arXiv)

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

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 151 references that are in the map. First posted on arXiv 7 Feb 2023.

Builds on

19 of 151 references.

References · 19

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7 more
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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Goodcited by 6