# Happiness Entailment: Automating Suggestions for Well-Being

## TL;DR

This paper proposes a neural network-based system to analyze journaled happy moments and suggest personalized actions to improve well-being, focusing on the development of the Happiness Entailment Recognition module.

## Contribution

It introduces a novel neural network model for happiness entailment recognition, enabling automated, personalized suggestions for well-being based on journal entries.

## Key findings

- The HER module can determine if a suggestion is likely beneficial based on a user's happy moment.
- The system captures psychologically significant features in texts for better suggestion relevance.
- Prototype demonstrates potential for automated well-being enhancement tools.

## Abstract

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.