Emoji Recommendation in Private Instant Messages

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 MultiLabel-RandomForest algorithm on real private instant message corpus. We want to determine the best discriminative features for this task.
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download

https://hal-amu.archives-ouvertes.fr/hal-01871071
Contributor : Gaël Guibon <>
Submitted on : Monday, September 10, 2018 - 12:13:33 PM
Last modification on : Monday, April 15, 2019 - 5:01:57 PM
Long-term archiving on : Tuesday, December 11, 2018 - 2:15:41 PM

File

emoji_prediction_instant_messa...
Files produced by the author(s)

Identifiers

Collections

Citation

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⟩

Share

Metrics

Record views

274

Files downloads

202