# HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments

arXiv: https://arxiv.org/abs/1801.07746 · HTML: https://mlsanity.com/paper/1801.07746/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2018.

## Built on by (most cited first, 9 of 9)

- [Goal Driven Discovery of Distributional Differences via Language Descriptions](https://mlsanity.com/paper/2302.14233/) (2023)
- [Goal-Driven Explainable Clustering via Language Descriptions](https://mlsanity.com/paper/2305.13749/) (2023)
- [How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact](https://mlsanity.com/paper/2106.02359/) (2021)
- [Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression](https://mlsanity.com/paper/2204.13569/) (2022)
- [Comparing Linear Probes with Mahalanobis Cosine Similarity](https://mlsanity.com/paper/2606.19603/) (2026)
- [Primary and Secondary Factor Consistency as Domain Knowledge to Guide Happiness Computing in Online Assessment](https://mlsanity.com/paper/2402.12398/) (2024)
- [S'olo Esc'uchame: Spanish Emotional Accompaniment Chatbot](https://mlsanity.com/paper/2408.01852/) (2024)
- [Random Forest-of-Thoughts: Uncertainty-aware Reasoning for Computational Social Science](https://mlsanity.com/paper/2502.18729/) (2025)
- [Interpretable Symptom Vectors for Depression in a Large Language Model](https://mlsanity.com/paper/2609.01832/) (2026)

## 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.