Abstract
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 Multi Label Random Forest algorithm on real private instant message corpus. We want to determine the best discriminative features for this task.
This work has obtained the Best Poster for the SONAMA track.
Gaël Guibon
Associate Professor
My research goes from emojis and emotion prediction and recommendation to meta learning, few-shot learning and French lexical evolution studies.