# Happiness Entailment: Automating Suggestions for Well-Being

arXiv: https://arxiv.org/abs/1907.10036 · HTML: https://mlsanity.com/paper/1907.10036/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 3 of 3)

- [How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact](https://mlsanity.com/paper/2106.02359/) (2021)
- [HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid](https://mlsanity.com/paper/2410.06370/) (2024)
- [Primary and Secondary Factor Consistency as Domain Knowledge to Guide Happiness Computing in Online Assessment](https://mlsanity.com/paper/2402.12398/) (2024)

## Abstract (arXiv)

Understanding what makes people happy is a central topic in psychology. Prior work has mostly focused on developing self-reporting assessment tools for individuals and relies on experts to analyze the periodic reported assessments. One of the goals of the analysis is to understand what actions are necessary to encourage modifications in the behaviors of the individuals to improve their overall well-being. In this paper, we outline a complementary approach; on the assumption that the user journals her happy moments as short texts, a system can analyze these texts and propose sustainable suggestions for the user that may lead to an overall improvement in her well-being. We prototype one necessary component of such a system, the Happiness Entailment Recognition (HER) module, which takes as input a short text describing an event, a candidate suggestion, and outputs a determination about whether the suggestion is more likely to be good for this user based on the event described. This component is implemented as a neural network model with two encoders, one for the user input and one for the candidate actionable suggestion, with additional layers to capture psychologically significant features in the happy moment and suggestion.