Jointly Learning to Detect Emotions and Predict Facebook Reactions
| Autor: | Marco Gori, Stefano Melacci, Lisa Graziani |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Emotion detection from text Computer Science - Machine Learning Computer science Emotion classification media_common.quotation_subject Facebook reactions Learning from Constraints Machine Learning (stat.ML) 02 engineering and technology Computer Science - Information Retrieval Machine Learning (cs.LG) Task (project management) 03 medical and health sciences 0302 clinical medicine Statistics - Machine Learning Human–computer interaction 0202 electrical engineering electronic engineering information engineering Social media Quality (business) media_common Focus (computing) Perspective (graphical) 020201 artificial intelligence & image processing Information Retrieval (cs.IR) 030217 neurology & neurosurgery |
| Zdroj: | Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series ISBN: 9783030304898 ICANN (4) |
| Popis: | 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. Comment: International Conference on Artificial Neural Networks. Springer, Cham, 2019 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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