# Natural Language Processing for Policymaking

arXiv: https://arxiv.org/abs/2302.03490 · HTML: https://mlsanity.com/paper/2302.03490/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 151 references that are in the map.
- First posted on arXiv 7 Feb 2023.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019)
- [How to Make Causal Inferences Using Texts](https://mlsanity.com/paper/1802.02163/) (2018)
- [Detecting Stance in Media on Global Warming](https://mlsanity.com/paper/2010.15149/) (2020)
- [Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs](https://mlsanity.com/paper/1904.00676/) (2019)
- [How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact](https://mlsanity.com/paper/2106.02359/) (2021)
- [SemEval-2015 Task 10: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02387/) (2019)
- [Analyzing Political Parody in Social Media](https://mlsanity.com/paper/2004.13878/) (2020)
- [Political Text Scaling Meets Computational Semantics](https://mlsanity.com/paper/1904.06217/) (2019)
- [NARMADA: Need and Available Resource Managing Assistant for Disasters and Adversities](https://mlsanity.com/paper/2005.13524/) (2020)
- [Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse](https://mlsanity.com/paper/2009.05560/) (2020)
- [An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events](https://mlsanity.com/paper/2006.01439/) (2020)
- [PolicyCLOUD: A prototype of a Cloud Serverless Ecosystem for Policy Analytics](https://mlsanity.com/paper/2201.06077/) (2022)

## Built on by (most cited first, 1 of 1)

- [Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good](https://mlsanity.com/paper/2305.05471/) (2023)

## 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