LIST OF PANELS

Natural Language Processing (NLP) offers transformative potential for public policy research, enabling innovative analysis of large volumes of textual data from news, official documents, social media, and other sources. This full-day introductory course provides a comprehensive overview of NLP techniques, including rule-based approaches, unsupervised and supervised learning, and the emerging capabilities of large language models (LLMs).

Through real-world examples and interactive hands-on exercises, participants will learn how NLP can be applied to key areas of public policy research, including understanding policy processes, analyzing policy design, and evaluating policy outcomes. For example, attendees will explore how sentiment analysis can reveal public opinion towards a new policy and how topic modeling can uncover trends in ongoing policy debates.

While this course is designed for researchers and practitioners from diverse backgrounds, a basic working knowledge of Python or R would be helpful for participating in the hands-on coding exercises. Participants with limited programming experience can leverage pre-written code templates and practical tools to implement NLP techniques, enabling them to apply these methods to their own research.

Attendees will leave the course with a strong foundational understanding of NLP techniques, diverse applications across geographies and domains, and actionable insights for leveraging NLP to address complex policy challenges.

Preconference Course