# Are Emojis Predictable?

arXiv: https://arxiv.org/abs/1702.07285 · HTML: https://mlsanity.com/paper/1702.07285/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2017.

## Built on by (most cited first, 6 of 6)

- [Towards Expressive Communication with Internet Memes: A New Multimodal Conversation Dataset and Benchmark](https://mlsanity.com/paper/2109.01839/) (2021)
- [SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research](https://mlsanity.com/paper/2310.14757/) (2023)
- [On-Device Emoji Classifier Trained with GPT-based Data Augmentation for a Mobile Keyboard](https://mlsanity.com/paper/2411.05031/) (2024)
- [Machine learning and emoji prediction: How much accuracy can MARBERT achieve?](https://mlsanity.com/paper/2604.21108/) (2026)
- [Selecting Stickers in Open-Domain Dialogue through Multitask Learning](https://mlsanity.com/paper/2209.07697/) (2022)
- [Contrastive Learning-based Multi Modal Architecture for Emoticon Prediction by Employing Image-Text Pairs](https://mlsanity.com/paper/2408.02571/) (2024)

## Abstract (arXiv)

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.