# Jointly Learning to Detect Emotions and Predict Facebook Reactions

arXiv: https://arxiv.org/abs/1909.10779 · HTML: https://mlsanity.com/paper/1909.10779/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review](https://mlsanity.com/paper/2202.12205/) (2022)
- [Seeking Sinhala Sentiment: Predicting Facebook Reactions of Sinhala Posts](https://mlsanity.com/paper/2112.00468/) (2021)
- [Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook Data](https://mlsanity.com/paper/2201.03941/) (2022)
- [Sinhala Sentence Embedding: A Two-Tiered Structure for Low-Resource Languages](https://mlsanity.com/paper/2210.14472/) (2022)
- ["That's so cute!": The CARE Dataset for Affective Response Detection](https://mlsanity.com/paper/2201.11895/) (2022)

## Abstract (arXiv)

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.