@inproceedings{stoikos-izbicki-2020-multilingual,
title = "Multilingual Emoticon Prediction of Tweets about {COVID}-19",
author = "Stoikos, Stefanos and
Izbicki, Mike",
booktitle = "Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.peoples-1.11",
pages = "109--118",
abstract = "Emojis are a widely used tool for encoding emotional content in informal messages such as tweets,and predicting which emoji corresponds to a piece of text can be used as a proxy for measuring the emotional content in the text. This paper presents the first model for predicting emojis in highly multilingual text.Our BERTmoticon model is a fine-tuned version of the BERT model,and it can predict emojis for text written in 102 different languages.We trained our BERTmoticon model on 54.2 million geolocated tweets sent in the first 6 months of 2020,and we apply the model to a case study analyzing the emotional reaction of Twitter users to news about the coronavirus. Example findings include a spike in sadness when the World Health Organization (WHO) declared that coronavirus was a global pandemic, and a spike in anger and disgust when the number of COVID-19 related deaths in the United States surpassed one hundred thousand. We provide an easy-to-use and open source python library for predicting emojis with BERTmoticon so that the model can easily be applied to other data mining tasks.",
}
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%0 Conference Proceedings
%T Multilingual Emoticon Prediction of Tweets about COVID-19
%A Stoikos, Stefanos
%A Izbicki, Mike
%S Proceedings of the Third Workshop on Computational Modeling of People’s Opinions, Personality, and Emotion’s in Social Media
%D 2020
%8 dec
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F stoikos-izbicki-2020-multilingual
%X Emojis are a widely used tool for encoding emotional content in informal messages such as tweets,and predicting which emoji corresponds to a piece of text can be used as a proxy for measuring the emotional content in the text. This paper presents the first model for predicting emojis in highly multilingual text.Our BERTmoticon model is a fine-tuned version of the BERT model,and it can predict emojis for text written in 102 different languages.We trained our BERTmoticon model on 54.2 million geolocated tweets sent in the first 6 months of 2020,and we apply the model to a case study analyzing the emotional reaction of Twitter users to news about the coronavirus. Example findings include a spike in sadness when the World Health Organization (WHO) declared that coronavirus was a global pandemic, and a spike in anger and disgust when the number of COVID-19 related deaths in the United States surpassed one hundred thousand. We provide an easy-to-use and open source python library for predicting emojis with BERTmoticon so that the model can easily be applied to other data mining tasks.
%U https://aclanthology.org/2020.peoples-1.11
%P 109-118
Markdown (Informal)
[Multilingual Emoticon Prediction of Tweets about COVID-19](https://aclanthology.org/2020.peoples-1.11) (Stoikos & Izbicki, PEOPLES 2020)
ACL
- Stefanos Stoikos and Mike Izbicki. 2020. Multilingual Emoticon Prediction of Tweets about COVID-19. In Proceedings of the Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotion's in Social Media, pages 109–118, Barcelona, Spain (Online). Association for Computational Linguistics.