Are Emojis Predictable?

2017/02/23 by Francesco Barbieri, Miguel Ballesteros, Barbieri, Francesco +3
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #Digital Communication and Language #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining

paper · doi:10.48550/arxiv.1702.07285

openalex publication_date 2017/02/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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.

Citations

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Long Short-Term Memory
• Exploiting Similarities among Languages for Machine Translation
• Probabilistic topic models

Related

• The New Modality: Emoji Challenges in Prediction, Anticipation, and Retrieval
• Emoji Prediction: Extensions and Benchmarking
• Emoji Prediction in Tweets using BERT
• A `Sourceful' Twist: Emoji Prediction Based on Sentiment, Hashtags and Application Source
• Understanding Textual Emotion Through Emoji Prediction