NLP for Affective Science

Affect refers to the fundamental neural processes that generate and regulate emotions, moods, and feeling states. Affect and emotions are central to how we organize meaning, to our behaviour, to our health and well-being, and to our very survival. Despite this, and even though most of us are all intimately familiar with emotions in everyday life, there is much we do not know about how emotions work, and how they impact our lives. Affective Science is a broad interdisciplinary field that explores these and other related questions about affect and emotions.

Since language is a powerful mechanism of emotion expression, there is great potential in using language data and computation to shed light on fundamental questions about emotions. However, even though much progress has been made in areas such as sentiment analysis and affective computing, much of the research focus is squarely on automatically classifying pieces of text.

In this tutorial, we present an introduction to Affective Science and argue that NLP is uniquely positioned to contribute to it: to boldly explore a new frontier — to use language and computation to ask fundamental questions about how emotions and affect work. We will cover the broad areas of research within this nascent field of study, Computational Affective Science (CAS), including:

• The theories and nature of affect
• The relationship of affect with the mind, body, and the world around us
• Affective data and resources
• Affective tasks and methods (including Generative AI)
• Applications
• Ethics, fairness, theory integration, and philosophical implications

We will also discuss specific case studies and key pieces of work within CAS on emotion dynamics, emotion granularity, affect lexicons, stereotype cognition models, and the language of interoception.

This tutorial presents a vision of Computational Affective Science that advances our understanding of emotion and human experience, builds useful applications, and plays an active role in navigating the societal implications of the powerful underlying technologies.