Paper Info
Title
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.
Year
DOI
Venue
2018
10.1145/3167132.3167430
SAC 2018: Symposium on Applied Computing Pau France April, 2018
Keywords
Field
DocType
emoji, messaging application, multi-label classification, natural language processing, recommendation
Recommender system,Emoji,Instant,Android (operating system),Information retrieval,Computer science,Multi-label classification,Discriminative model
Conference
ISBN
Citations 
PageRank 
978-1-4503-5191-1
0
0.34
References 
Authors
10
3
Name
Order
Citations
PageRank
Gaël Guibon100.34
Magalie Ochs232635.76
Patrice Bellot324151.18