@inproceedings{boy-etal-2021-emoji,
title = "Emoji-Based Transfer Learning for Sentiment Tasks",
author = "Boy, Susann and
Ruiter, Dana and
Klakow, Dietrich",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-srw.15",
doi = "10.18653/v1/2021.eacl-srw.15",
pages = "103--110",
abstract = "Sentiment tasks such as hate speech detection and sentiment analysis, especially when performed on languages other than English, are often low-resource. In this study, we exploit the emotional information encoded in emojis to enhance the performance on a variety of sentiment tasks. This is done using a transfer learning approach, where the parameters learned by an emoji-based source task are transferred to a sentiment target task. We analyse the efficacy of the transfer under three conditions, i.e. i) the emoji content and ii) label distribution of the target task as well as iii) the difference between monolingually and multilingually learned source tasks. We find i.a. that the transfer is most beneficial if the target task is balanced with high emoji content. Monolingually learned source tasks have the benefit of taking into account the culturally specific use of emojis and gain up to F1 +0.280 over the baseline.",
}
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<abstract>Sentiment tasks such as hate speech detection and sentiment analysis, especially when performed on languages other than English, are often low-resource. In this study, we exploit the emotional information encoded in emojis to enhance the performance on a variety of sentiment tasks. This is done using a transfer learning approach, where the parameters learned by an emoji-based source task are transferred to a sentiment target task. We analyse the efficacy of the transfer under three conditions, i.e. i) the emoji content and ii) label distribution of the target task as well as iii) the difference between monolingually and multilingually learned source tasks. We find i.a. that the transfer is most beneficial if the target task is balanced with high emoji content. Monolingually learned source tasks have the benefit of taking into account the culturally specific use of emojis and gain up to F1 +0.280 over the baseline.</abstract>
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%0 Conference Proceedings
%T Emoji-Based Transfer Learning for Sentiment Tasks
%A Boy, Susann
%A Ruiter, Dana
%A Klakow, Dietrich
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2021
%8 apr
%I Association for Computational Linguistics
%C Online
%F boy-etal-2021-emoji
%X Sentiment tasks such as hate speech detection and sentiment analysis, especially when performed on languages other than English, are often low-resource. In this study, we exploit the emotional information encoded in emojis to enhance the performance on a variety of sentiment tasks. This is done using a transfer learning approach, where the parameters learned by an emoji-based source task are transferred to a sentiment target task. We analyse the efficacy of the transfer under three conditions, i.e. i) the emoji content and ii) label distribution of the target task as well as iii) the difference between monolingually and multilingually learned source tasks. We find i.a. that the transfer is most beneficial if the target task is balanced with high emoji content. Monolingually learned source tasks have the benefit of taking into account the culturally specific use of emojis and gain up to F1 +0.280 over the baseline.
%R 10.18653/v1/2021.eacl-srw.15
%U https://aclanthology.org/2021.eacl-srw.15
%U https://doi.org/10.18653/v1/2021.eacl-srw.15
%P 103-110
Markdown (Informal)
[Emoji-Based Transfer Learning for Sentiment Tasks](https://aclanthology.org/2021.eacl-srw.15) (Boy et al., EACL 2021)
ACL
- Susann Boy, Dana Ruiter, and Dietrich Klakow. 2021. Emoji-Based Transfer Learning for Sentiment Tasks. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 103–110, Online. Association for Computational Linguistics.