Emoji Recommendation in Private Instant Messages

Gaël Guibon (1, 2, 3, 4, 5) , Magalie Ochs (1, 2, 3, 4, 5) , Patrice Bellot (1, 2, 3, 4, 5)

1 DIMAG - Data, Information & content MAnagement Group
2 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)
3 AMU - Aix Marseille Université
4 R2I - Recherche d’information et Interactions
5 ILCB - Institute of Language, Communication and the Brain

Emojis are some of the most common ways to convey emotions and sentiments in social messaging applications. In order to help the user choose emojis among a vast range of possibilities, we aim at developing an automatic recommendation system based on user message analysis and real emoji usage, which goes beyond the simple dictionnary lookup that is done in the industry (mainly Android and iOS). For this purpose, we present a novel automatic emoji prediction model trained and tested on real data and based on sentiment-related features. Such a model differ from the ones learnt from tweets and can predict emojis with a 84.48% f1-score and a 95.49% high precision, using MultiLabel-RandomForest algorithm on real private instant message corpus. We want to determine the best discriminative features for this task.

Mots clés: natural language processing; Emoji recommendation; CCS CONCEPTS • Computing methodologies → Information extraction; multi-label classification; messaging application; Emoji

Domaines: Informatique et langage [cs.CL]; Apprentissage [cs.LG]; Réseaux sociaux et d'information [cs.SI]; Intelligence artificielle [cs.AI]

Gaël Guibon, Magalie Ochs, Patrice Bellot. Emoji Recommendation in Private Instant Messages. SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Apr 2018, Pau, France. ⟨10.1145/3167132.3167430⟩. ⟨hal-01871071⟩