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        <namePart type="given">Muhammad</namePart>
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            <namePart type="family">Barzilay</namePart>
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    <abstract>Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives. However, progress on emotion detection has been hampered by the absence of large labeled datasets. In this work, we build a very large dataset for fine-grained emotions and develop deep learning models on it. We achieve a new state-of-the-art on 24 fine-grained types of emotions (with an average accuracy of 87.58%). We also extend the task beyond emotion types to model Robert Plutick’s 8 primary emotion dimensions, acquiring a superior accuracy of 95.68%.</abstract>
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%0 Conference Proceedings
%T EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks
%A Abdul-Mageed, Muhammad
%A Ungar, Lyle
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F abdul-mageed-ungar-2017-emonet
%X Accurate detection of emotion from natural language has applications ranging from building emotional chatbots to better understanding individuals and their lives. However, progress on emotion detection has been hampered by the absence of large labeled datasets. In this work, we build a very large dataset for fine-grained emotions and develop deep learning models on it. We achieve a new state-of-the-art on 24 fine-grained types of emotions (with an average accuracy of 87.58%). We also extend the task beyond emotion types to model Robert Plutick’s 8 primary emotion dimensions, acquiring a superior accuracy of 95.68%.
%R 10.18653/v1/P17-1067
%U https://aclanthology.org/P17-1067
%U https://doi.org/10.18653/v1/P17-1067
%P 718-728

Markdown (Informal)

[EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks](https://aclanthology.org/P17-1067) (Abdul-Mageed & Ungar, ACL 2017)

• EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks (Abdul-Mageed & Ungar, ACL 2017)

ACL

• Muhammad Abdul-Mageed and Lyle Ungar. 2017. EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 718–728, Vancouver, Canada. Association for Computational Linguistics.