HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments
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Abstract (arXiv)
The science of happiness is an area of positive psychology concerned with understanding what behaviors make people happy in a sustainable fashion. Recently, there has been interest in developing technologies that help incorporate the findings of the science of happiness into users' daily lives by steering them towards behaviors that increase happiness. With the goal of building technology that can understand how people express their happy moments in text, we crowd-sourced HappyDB, a corpus of 100,000 happy moments that we make publicly available. This paper describes HappyDB and its properties, and outlines several important NLP problems that can be studied with the help of the corpus. We also apply several state-of-the-art analysis techniques to analyze HappyDB. Our results demonstrate the need for deeper NLP techniques to be developed which makes HappyDB an exciting resource for follow-on research.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2; 2025: 1; 2026: 2.
9 papers.
- 2023Goal Driven Discovery of Distributional Differences via Language Descriptionscited by 61
- 2023Goal-Driven Explainable Clustering via Language Descriptionscited by 58
- 2021How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impactcited by 18
- 2022Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depressioncited by 1
- 2026Comparing Linear Probes with Mahalanobis Cosine Similaritycited by 1
- 2024Primary and Secondary Factor Consistency as Domain Knowledge to Guide Happiness Computing in Online Assessmentcited by 0
- 2024S'olo Esc'uchame: Spanish Emotional Accompaniment Chatbotcited by 0
- 2025Random Forest-of-Thoughts: Uncertainty-aware Reasoning for Computational Social Sciencecited by 0
- 2026Interpretable Symptom Vectors for Depression in a Large Language Modelcited by 0
- 2026Interpretable Symptom Vectors for Depression in a Large Language Model1 Sep 2026
- 2026Comparing Linear Probes with Mahalanobis Cosine Similarity17 Jun 2026
- 2025Random Forest-of-Thoughts: Uncertainty-aware Reasoning for Computational Social Science26 Feb 2025
- 2024S'olo Esc'uchame: Spanish Emotional Accompaniment Chatbot3 Aug 2024
- 2024Primary and Secondary Factor Consistency as Domain Knowledge to Guide Happiness Computing in Online Assessment17 Feb 2024
- 2023Goal-Driven Explainable Clustering via Language Descriptions23 May 2023
- 2023Goal Driven Discovery of Distributional Differences via Language Descriptions28 Feb 2023
- 2022Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression28 Apr 2022
- 2021How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact4 Jun 2021