@inproceedings{kwon-etal-2021-making,
title = "Making Your Tweets More Fancy: Emoji Insertion to Texts",
author = "Kwon, Jingun and
Kobayashi, Naoki and
Kamigaito, Hidetaka and
Takamura, Hiroya and
Okumura, Manabu",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.88",
pages = "770--779",
abstract = "In the social media, users frequently use small images called emojis in their posts. Although using emojis in texts plays a key role in recent communication systems, less attention has been paid on their positions in the given texts, despite that users carefully choose and put an emoji that matches their post. Exploring positions of emojis in texts will enhance understanding of the relationship between emojis and texts. We extend an emoji label prediction task taking into account the information of emoji positions, by jointly learning the emoji position in a tweet to predict the emoji label. The results demonstrate that the position of emojis in texts is a good clue to boost the performance of emoji label prediction. Human evaluation validates that there exists a suitable emoji position in a tweet, and our proposed task is able to make tweets more fancy and natural. In addition, considering emoji position can further improve the performance for the irony detection task compared to the emoji label prediction. We also report the experimental results for the modified dataset, due to the problem of the original dataset for the first shared task to predict an emoji label in SemEval2018.",
}
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<abstract>In the social media, users frequently use small images called emojis in their posts. Although using emojis in texts plays a key role in recent communication systems, less attention has been paid on their positions in the given texts, despite that users carefully choose and put an emoji that matches their post. Exploring positions of emojis in texts will enhance understanding of the relationship between emojis and texts. We extend an emoji label prediction task taking into account the information of emoji positions, by jointly learning the emoji position in a tweet to predict the emoji label. The results demonstrate that the position of emojis in texts is a good clue to boost the performance of emoji label prediction. Human evaluation validates that there exists a suitable emoji position in a tweet, and our proposed task is able to make tweets more fancy and natural. In addition, considering emoji position can further improve the performance for the irony detection task compared to the emoji label prediction. We also report the experimental results for the modified dataset, due to the problem of the original dataset for the first shared task to predict an emoji label in SemEval2018.</abstract>
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%0 Conference Proceedings
%T Making Your Tweets More Fancy: Emoji Insertion to Texts
%A Kwon, Jingun
%A Kobayashi, Naoki
%A Kamigaito, Hidetaka
%A Takamura, Hiroya
%A Okumura, Manabu
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 sep
%I INCOMA Ltd.
%C Held Online
%F kwon-etal-2021-making
%X In the social media, users frequently use small images called emojis in their posts. Although using emojis in texts plays a key role in recent communication systems, less attention has been paid on their positions in the given texts, despite that users carefully choose and put an emoji that matches their post. Exploring positions of emojis in texts will enhance understanding of the relationship between emojis and texts. We extend an emoji label prediction task taking into account the information of emoji positions, by jointly learning the emoji position in a tweet to predict the emoji label. The results demonstrate that the position of emojis in texts is a good clue to boost the performance of emoji label prediction. Human evaluation validates that there exists a suitable emoji position in a tweet, and our proposed task is able to make tweets more fancy and natural. In addition, considering emoji position can further improve the performance for the irony detection task compared to the emoji label prediction. We also report the experimental results for the modified dataset, due to the problem of the original dataset for the first shared task to predict an emoji label in SemEval2018.
%U https://aclanthology.org/2021.ranlp-1.88
%P 770-779
Markdown (Informal)
[Making Your Tweets More Fancy: Emoji Insertion to Texts](https://aclanthology.org/2021.ranlp-1.88) (Kwon et al., RANLP 2021)
ACL
- Jingun Kwon, Naoki Kobayashi, Hidetaka Kamigaito, Hiroya Takamura, and Manabu Okumura. 2021. Making Your Tweets More Fancy: Emoji Insertion to Texts. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 770–779, Held Online. INCOMA Ltd..