At its third edition, this workshop aims to identify the leading natural language processing methods currently used in economics, and develop discussions around how they are and should be implemented.

The workshop will take place across two days (the exact start and end times are TBC). We will add a full programme to this page soon.

Call for papers

We aim for the workshop to provide a collaborative and collegial setting for researchers to present and discuss ongoing work.

We therefore invite submissions of papers applying natural language processing (NLP) methods to the analysis of text. Examples are:

algorithms for reduction of dimensionality (e.g., bag-of-word models, word and doc. embeddings);

unsupervised classification methods (e.g., clustering algorithms, unsupervised topic models);

supervised classification methods (e.g., semi-supervised and supervised topic models);

large language models and transformer-based methods (e.g., classification, information extraction, and annotation using models such as BERT and GPT).

How to submit your paper

Please submit your paper or extended abstract of two to three pages through our form

The deadline to submit is 24 May 2026.

If you have any issues submitting, please email us at text.as.data.bath.workshop@gmail.com

Please specify your name, affiliation, and the type(s) of NLP method(s) you use.

Notification of acceptance

Successful authors will be notified about the acceptance of their paper by 1 June, 2026. Invited speakers will be required to fund their travel and accommodation expenses.

Book your tickets

Registration for this workshop will open soon. Please check this page regularly for updates.

This workshop will be free to attend, but we will require attendees to register.

Keynote speakers

We will host two keynote speakers.

Professor Daniel Chen

Institute For Advanced Study in Toulouse (IAST), Toulouse School of Economics

Daniel is a professor at the Toulouse School of Economics (TSE) and works at the intersection of law and economics, data science, machine learning, causal inference, and justice. His work has been published in leading outlets including the American Economic Review, Econometrica, Journal of Political Economy, Quarterly Journal of Economics, PNAS, Science Advances, and Nature Human Behaviour, and has been covered by the New York Times, Washington Post, Wall Street Journal, and NPR.

Dr Milena Djourelova

University of Southern California

Milena is an Assistant Professor of Economics at USC and a CESifo Research Affiliate. Her research is in applied microeconomics and political economy, with a focus on information, media, and electoral politics. Her work has been published in leading outlets, including the American Economic Review, American Journal of Political Science, and Review of Economic Studies.