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        <title>Sarcasm Identification and Detection in Conversion Context using BERT</title>
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            <title>Proceedings of the Second Workshop on Figurative Language Processing</title>
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            <namePart type="given">Beata</namePart>
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    <abstract>Sarcasm analysis in user conversion text is automatic detection of any irony, insult, hurting, painful, caustic, humour, vulgarity that degrades an individual. It is helpful in the field of sentimental analysis and cyberbullying. As an immense growth of social media, sarcasm analysis helps to avoid insult, hurts and humour to affect someone. In this paper, we present traditional machine learning approaches, deep learning approach (LSTM -RNN) and BERT (Bidirectional Encoder Representations from Transformers) for identifying sarcasm. We have used the approaches to build the model, to identify and categorize how much conversion context or response is needed for sarcasm detection and evaluated on the two social media forums that is twitter conversation dataset and reddit conversion dataset. We compare the performance based on the approaches and obtained the best F1 scores as 0.722, 0.679 for the twitter forums and reddit forums respectively.</abstract>
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    <identifier type="doi">10.18653/v1/2020.figlang-1.10</identifier>
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%0 Conference Proceedings
%T Sarcasm Identification and Detection in Conversion Context using BERT
%A A., Kalaivani
%A D., Thenmozhi
%Y Klebanov, Beata Beigman
%Y Shutova, Ekaterina
%Y Lichtenstein, Patricia
%Y Muresan, Smaranda
%Y Wee, Chee
%Y Feldman, Anna
%Y Ghosh, Debanjan
%S Proceedings of the Second Workshop on Figurative Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F a-d-2020-sarcasm
%X Sarcasm analysis in user conversion text is automatic detection of any irony, insult, hurting, painful, caustic, humour, vulgarity that degrades an individual. It is helpful in the field of sentimental analysis and cyberbullying. As an immense growth of social media, sarcasm analysis helps to avoid insult, hurts and humour to affect someone. In this paper, we present traditional machine learning approaches, deep learning approach (LSTM -RNN) and BERT (Bidirectional Encoder Representations from Transformers) for identifying sarcasm. We have used the approaches to build the model, to identify and categorize how much conversion context or response is needed for sarcasm detection and evaluated on the two social media forums that is twitter conversation dataset and reddit conversion dataset. We compare the performance based on the approaches and obtained the best F1 scores as 0.722, 0.679 for the twitter forums and reddit forums respectively.
%R 10.18653/v1/2020.figlang-1.10
%U https://aclanthology.org/2020.figlang-1.10
%U https://doi.org/10.18653/v1/2020.figlang-1.10
%P 72-76

Markdown (Informal)

[Sarcasm Identification and Detection in Conversion Context using BERT](https://aclanthology.org/2020.figlang-1.10) (A. & D., Fig-Lang 2020)

• Sarcasm Identification and Detection in Conversion Context using BERT (A. & D., Fig-Lang 2020)

ACL

• Kalaivani A. and Thenmozhi D.. 2020. Sarcasm Identification and Detection in Conversion Context using BERT. In Proceedings of the Second Workshop on Figurative Language Processing, pages 72–76, Online. Association for Computational Linguistics.