Natural Language Processing for Policymaking
It provides a review for policymakers and researchers on using NLP in computational social science, but is incremental as it synthesizes existing methods without new results.
This chapter introduces common NLP methods like text classification and topic modeling, and overviews their applications in policymaking for data collection, decision interpretation, communication, and effect investigation, while highlighting limitations and ethical concerns.
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