Exploring Emoji Usage and Prediction Through a Temporal Variation Lens
This work addresses the challenge of improving multimodal communication systems by accounting for temporal variations in emoji usage, though it is incremental as it builds on existing emoji prediction methods.
The paper tackled the problem of emoji usage and semantics varying over time, showing that emoji embeddings differ by season and that incorporating time information into prediction systems outperforms state-of-the-art methods, with significant accuracy improvements for some emojis.
The frequent use of Emojis on social media platforms has created a new form of multimodal social interaction. Developing methods for the study and representation of emoji semantics helps to improve future multimodal communication systems. In this paper, we explore the usage and semantics of emojis over time. We compare emoji embeddings trained on a corpus of different seasons and show that some emojis are used differently depending on the time of the year. Moreover, we propose a method to take into account the time information for emoji prediction systems, outperforming state-of-the-art systems. We show that, using the time information, the accuracy of some emojis can be significantly improved.