Jointly Learning to Detect Emotions and Predict Facebook Reactions
This work addresses emotion detection and reaction prediction for social media analysis, presenting an incremental improvement by integrating logic constraints into a neural model.
The paper tackles the problem of jointly detecting emotions and predicting Facebook reactions by proposing an end-to-end neural model that uses First-Order Logic formulas as polynomial constraints in a multi-task setup. The result shows that both tasks benefit from their interaction, as demonstrated through an experimental analysis on a large collection of Facebook posts.
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.