Jointly Learning to Detect Emotions and Predict Facebook Reactions

TL;DR

This paper introduces an end-to-end neural model that jointly detects emotions and predicts Facebook reactions, leveraging logic constraints and multi-task learning to improve performance on social media data.

Contribution

It presents a novel multi-task neural approach that integrates emotion detection and reaction prediction using logic-based constraints, enhancing both tasks.

Findings

Joint learning improves emotion classification accuracy.

Reaction prediction benefits from emotion detection.

Logic constraints enhance model interpretability.

Abstract

The growing ubiquity of Social Media data offers an attractive perspective for improving the quality of machine learning-based models in several fields, ranging from Computer Vision to Natural Language Processing. In this paper we focus on Facebook posts paired with reactions of multiple users, and we investigate their relationships with classes of emotions that are typically considered in the task of emotion detection. We are inspired by the idea of introducing a connection between reactions and emotions by means of First-Order Logic formulas, and we propose an end-to-end neural model that is able to jointly learn to detect emotions and predict Facebook reactions in a multi-task environment, where the logic formulas are converted into polynomial constraints. Our model is trained using a large collection of unsupervised texts together with data labeled with emotion classes and Facebook posts that include reactions. An extended experimental analysis that leverages a large collection of Facebook posts shows that the tasks of emotion classification and reaction prediction can both benefit from their interaction.