Are Emojis Predictable?

Abstract

Emojis are ideograms which are naturally combined with plain text to visually complement or condense the meaning of a message. Despite being widely used in social media, their underlying semantics have received little attention from a Natural Language Processing standpoint. In this paper, we investigate the relation between words and emojis, studying the novel task of predicting which emojis are evoked by text-based tweet messages. We train several models based on Long Short-Term Memory networks (LSTMs) in this task. Our experimental results show that our neural model outperforms two baselines as well as humans solving the same task, suggesting that computational models are able to better capture the underlying semantics of emojis.

To appear at EACL 2017

References in corpus (3)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Exploiting Similarities among Languages for Machine Translation
• Transition-Based Dependency Parsing with Stack Long Short-Term Memory

Cited by in corpus (6)

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• Exploring Emoji Usage and Prediction Through a Temporal Variation Lens
• Mining the Relationship between Emoji Usage Patterns and Personality
• Learning Emoji Embeddings using Emoji Co-occurrence Network Graph
• Word-Emoji Embeddings from large scale Messaging Data reflect real-world Semantic Associations of Expressive Icons
• Understanding Chat Messages for Sticker Recommendation in Messaging Apps