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Jointly Learning to Detect Emotions and Predict Facebook Reactions

  • Lisa GrazianiEmail author
  • Stefano Melacci
  • Marco Gori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11730)

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.

Keywords

Emotion detection from text Facebook reactions Learning from Constraints 

Notes

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 825619.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DINFOUniversity of FlorenceFlorenceItaly
  2. 2.DIISMUniversity of SienaSienaItaly

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