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.
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SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Apr 2018, Pau, France. ACM Press, 2018, SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing. 〈10.1145/3167132.3167430〉
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Dernière modification le : mardi 26 février 2019 - 21:50:55
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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. ACM Press, 2018, SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing. 〈10.1145/3167132.3167430〉. 〈hal-01871071〉

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