@inproceedings{barbieri-etal-2017-emojis,
title = "Are Emojis Predictable?",
author = "Barbieri, Francesco and
Ballesteros, Miguel and
Saggion, Horacio",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2017",
pages = "105--111",
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 a baseline as well as humans solving the same task, suggesting that computational models are able to better capture the underlying semantics of emojis.",
}
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%0 Conference Proceedings
%T Are Emojis Predictable?
%A Barbieri, Francesco
%A Ballesteros, Miguel
%A Saggion, Horacio
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F barbieri-etal-2017-emojis
%X 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 a baseline as well as humans solving the same task, suggesting that computational models are able to better capture the underlying semantics of emojis.
%U https://aclanthology.org/E17-2017
%P 105-111
Markdown (Informal)
[Are Emojis Predictable?](https://aclanthology.org/E17-2017) (Barbieri et al., EACL 2017)
ACL
- Francesco Barbieri, Miguel Ballesteros, and Horacio Saggion. 2017. Are Emojis Predictable?. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 105–111, Valencia, Spain. Association for Computational Linguistics.