HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments

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Authors Behzad Golshan, Sara Evensen, Akari Asai, Andrei Lopatenko, Yoshihiko Suhara, Wang-Chiew Tan, Yinzhan Xu, Alon Halevy, Vivian Li, Daniela Stepanov
Journal/Conference Name LREC 2018 5
Paper Category
Paper Abstract 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.
Date of publication 2018
Code Programming Language Multiple
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