Abstract
Emojis are becoming a new visual and linguistic tool that allows users to express their feelings and communicate with each other on social media. Driven by the importance of emoji interpretation, an emoji prediction task, which aims to predict the most likely emojis within a text, has gained significant attention. Thus, we propose a new deep neural network model for this task, MultiEmo (multi-task framework for emoji prediction), to predict the most relevant emoji by considering the emotion detection task. Our experiment shows that MultiEmo is superior to existing models using the Twitter dataset, implying that the models can learn richer representations from semantically related tasks. We systematically confirm that each emoji is associated with a particular emotion in a similar context. We also introduce new evaluation metrics to measure the comprehensive performance of the models.
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