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. Our model achieves an AU-ROC of 0.831 and outperforms our baseline as well as the current state-of-the-art Textual Entailment model from AllenNLP by more than 48% of improvements, confirming the uniqueness and complexity of the HER task.

Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price, and cuisine, and any experiential data is relegated to text reviews. In order to support experiential queries, a database system needs to model subjective data. Users should be able to pose queries that specify subjective experiences using their own words, in addition to conditions on the usual objective attributes. This paper introduces OpineDB, a subjective database system that addresses these challenges. We introduce a data model for subjective databases. We describe how OpineDB translates subjective queries against the subjective database schema, which is done by matching the user query phrases to the underlying schema. We also show how the experiential conditions specified by the user can be combined and the results aggregated and ranked. We demonstrate that subjective databases satisfy user needs more effectively and accurately than alternative techniques through experiments with real data of hotel and restaurant reviews.