Linguistic Properties of Emojis: A Quantitative Exploration of Emoji Frequency, Category, and Position on Twitter

In social media, the frequent use of small images, called emojis, in posts has played a key role in recent communications. However, less attention has been paid to their positions in the given texts although users are known to carefully choose and place emojis that match their post. Exploring the position of emojis in texts is expected to enhance our understanding of the relationship between emojis and texts. In this paper, we propose a novel task of inserting an emoji at a position in a given tweet. We extend an emoji label prediction method considering the information of emoji positions, by jointly learning the emoji position in a tweet to predict the emoji label. Additional information on emoji position can improve the performance of emoji prediction. Human evaluations validate the existence of a suitable emoji position in a tweet. The proposed task makes tweets fancier and more natural. In addition, the emoji position can further improve the performance of irony detection compared to emoji label prediction. We also report the experimental results for the modified dataset, due to the problem of the original dataset for the first shared task to predict an emoji label in SemEval 2018. 

Joint Modeling of Emoji Position and Its Label for Better Understanding in Social Media

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Joint Modeling of Emoji Position and Its Label for Better Understanding in Social Media