Book contents
- The Cambridge Handbook of Behavioural Data Science
- Cambridge Handbooks in Psychology
- Reviews
- The Cambridge Handbook of Behavioural Data Science
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Introduction
- Part I Introduction to Behavioural Data Science
- Part II Human Behaviour
- Part III Algorithmic Behaviour
- Part IV Systems and Culture
- Part V Applications
- 29 Applications of Behavioural Data Science
- 30 Pro-Social Nudging
- 31 Social Media Analytics
- 32 Quantifying Luck
- 33 Quantifying the Connection between Scenic Beauty and Reported Health Using Deep Learning and Econometrics
- 34 Money, Methodology and Happiness
- 35 Human–Data Interaction
- 36 Natural Language Processing in Behavioural Data Science
- 37 Understanding Collective Behaviour Using Online Data and Mobile Phones
- 38 Burstier Events
- 39 Behavioural Data Science in Financial Services
- 40 XR, VR and AR Applications in Behavioural Data Science
- 41 On Cryptoasset Traders’ Behaviour
- 42 Behavioural Data Science of Cybersecurity
- 43 Behavioural Data Science Ethics and Governance Pre-AI Act
- 44 Behavioural Data Science Ethics and Governance Post-AI Act
- Part VI Concluding Remarks
- Main Abbreviations and Acronyms
- Glossary
- References
36 - Natural Language Processing in Behavioural Data Science
Using Computational Linguistics to Understand and Model Behaviour
from Part V - Applications
Published online by Cambridge University Press: 29 May 2026
Book contents
- The Cambridge Handbook of Behavioural Data Science
- Cambridge Handbooks in Psychology
- Reviews
- The Cambridge Handbook of Behavioural Data Science
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Introduction
- Part I Introduction to Behavioural Data Science
- Part II Human Behaviour
- Part III Algorithmic Behaviour
- Part IV Systems and Culture
- Part V Applications
- 29 Applications of Behavioural Data Science
- 30 Pro-Social Nudging
- 31 Social Media Analytics
- 32 Quantifying Luck
- 33 Quantifying the Connection between Scenic Beauty and Reported Health Using Deep Learning and Econometrics
- 34 Money, Methodology and Happiness
- 35 Human–Data Interaction
- 36 Natural Language Processing in Behavioural Data Science
- 37 Understanding Collective Behaviour Using Online Data and Mobile Phones
- 38 Burstier Events
- 39 Behavioural Data Science in Financial Services
- 40 XR, VR and AR Applications in Behavioural Data Science
- 41 On Cryptoasset Traders’ Behaviour
- 42 Behavioural Data Science of Cybersecurity
- 43 Behavioural Data Science Ethics and Governance Pre-AI Act
- 44 Behavioural Data Science Ethics and Governance Post-AI Act
- Part VI Concluding Remarks
- Main Abbreviations and Acronyms
- Glossary
- References
Summary
Recent advances in data-driven behavioural science and, particularly, in Behavioural Data Science, saw the rise of applying natural language processing techniques to understanding and modelling human behaviour, algorithmic behaviour, as well as behaviour of complex human–machine systems. From associative modelling in the standard psychological experiments to understanding emotional arcs in novels and movies through the way humans, algorithms and complex systems use language, this chapter demonstrates how computational linguistics methods allow behavioural methodology to go beyond the ‘sterile’ laboratory environments into the field using data, test hypotheses at scale and have scalable practical impact. This chapter also shows how language-based modelling can shed light on cognition, decision-making, judgements and cultural evolution. Using empirical example of a large-scale Behavioural Data Science study, this chapter also demonstrates how language analysis can be used as a ‘truth serum’, helping to obtain underlying human preferences from subjective judgements usually provided in surveys.
Keywords
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- Type
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- Information
- The Cambridge Handbook of Behavioural Data Science , pp. 637 - 652Publisher: Cambridge University PressPrint publication year: 2026
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