Abstract English is the dominant language in the study of human cognition and behavior: the
individuals studied by cognitive scientists, as well as most of the scientists themselves, are …

This article provides an overview of the methods used for algorithmic text analysis in
economics, with a focus on three key contributions. First, we introduce methods for …

Recently, there has been an increased interest in demographically grounded bias in natural
language processing (NLP) applications. Much of the recent work has focused on describing …

A guide for using computational text analysis to learn about the social world From social
media posts and text messages to digital government documents and archives, researchers …

Large language models (LLMs) are capable of successfully performing many language
processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify …

Word embeddings are becoming popular for political science research, yet we know little
about their properties and performance. To help scholars seeking to use these techniques …

Using word embeddings from 850 billion words in English-language Google Books, we
provide an extensive analysis of historical change and stability in social group …

We develop a deep learning model to detect emotions embedded in press conferences after
the Federal Open Market Committee meetings and examine the influence of the detected …

We commonly think of society as made of and by humans, but with the proliferation of
machine learning and AI technologies, this is clearly no longer the case. Billions of …

Natural language processing (NLP) applications are now more powerful and ubiquitous
than ever before. With rapidly developing (neural) models and ever-more available data …