@inproceedings{singh-etal-2019-incorporating,
title = "Incorporating Emoji Descriptions Improves Tweet Classification",
author = "Singh, Abhishek and
Blanco, Eduardo and
Jin, Wei",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1214",
doi = "10.18653/v1/N19-1214",
pages = "2096--2101",
abstract = "Tweets are short messages that often include specialized language such as hashtags and emojis. In this paper, we present a simple strategy to process emojis: replace them with their natural language description and use pretrained word embeddings as normally done with standard words. We show that this strategy is more effective than using pretrained emoji embeddings for tweet classification. Specifically, we obtain new state-of-the-art results in irony detection and sentiment analysis despite our neural network is simpler than previous proposals.",
}
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<abstract>Tweets are short messages that often include specialized language such as hashtags and emojis. In this paper, we present a simple strategy to process emojis: replace them with their natural language description and use pretrained word embeddings as normally done with standard words. We show that this strategy is more effective than using pretrained emoji embeddings for tweet classification. Specifically, we obtain new state-of-the-art results in irony detection and sentiment analysis despite our neural network is simpler than previous proposals.</abstract>
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%0 Conference Proceedings
%T Incorporating Emoji Descriptions Improves Tweet Classification
%A Singh, Abhishek
%A Blanco, Eduardo
%A Jin, Wei
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F singh-etal-2019-incorporating
%X Tweets are short messages that often include specialized language such as hashtags and emojis. In this paper, we present a simple strategy to process emojis: replace them with their natural language description and use pretrained word embeddings as normally done with standard words. We show that this strategy is more effective than using pretrained emoji embeddings for tweet classification. Specifically, we obtain new state-of-the-art results in irony detection and sentiment analysis despite our neural network is simpler than previous proposals.
%R 10.18653/v1/N19-1214
%U https://aclanthology.org/N19-1214
%U https://doi.org/10.18653/v1/N19-1214
%P 2096-2101
Markdown (Informal)
[Incorporating Emoji Descriptions Improves Tweet Classification](https://aclanthology.org/N19-1214) (Singh et al., NAACL 2019)
ACL
- Abhishek Singh, Eduardo Blanco, and Wei Jin. 2019. Incorporating Emoji Descriptions Improves Tweet Classification. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2096–2101, Minneapolis, Minnesota. Association for Computational Linguistics.