  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
