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        <title>An LSTM-Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data</title>
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    <name type="personal">
        <namePart type="given">Ashraf</namePart>
        <namePart type="family">Kamal</namePart>
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        <namePart type="given">Muhammad</namePart>
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            <title>Proceedings of the 16th International Conference on Natural Language Processing</title>
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            <namePart type="given">Dipti</namePart>
            <namePart type="given">Misra</namePart>
            <namePart type="family">Sharma</namePart>
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        <name type="personal">
            <namePart type="given">Pushpak</namePart>
            <namePart type="family">Bhattacharya</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <publisher>NLP Association of India</publisher>
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    <abstract>Self-deprecating sarcasm is a special category of sarcasm, which is nowadays popular and useful for many real-life applications, such as brand endorsement, product campaign, digital marketing, and advertisement. The self-deprecating style of campaign and marketing strategy is mainly adopted to excel brand endorsement and product sales value. In this paper, we propose an LSTM-based deep learning approach for detecting self-deprecating sarcasm in textual data. To the best of our knowledge, there is no prior work related to self-deprecating sarcasm detection using deep learning techniques. Starting with a filtering step to identify self-referential tweets, the proposed approach adopts a deep learning model using LSTM for detecting self-deprecating sarcasm. The proposed approach is evaluated over three Twitter datasets and performs significantly better in terms of precision, recall, and f-score.</abstract>
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%0 Conference Proceedings
%T An LSTM-Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data
%A Kamal, Ashraf
%A Abulaish, Muhammad
%Y Sharma, Dipti Misra
%Y Bhattacharya, Pushpak
%S Proceedings of the 16th International Conference on Natural Language Processing
%D 2019
%8 December
%I NLP Association of India
%C International Institute of Information Technology, Hyderabad, India
%F kamal-abulaish-2019-lstm
%X Self-deprecating sarcasm is a special category of sarcasm, which is nowadays popular and useful for many real-life applications, such as brand endorsement, product campaign, digital marketing, and advertisement. The self-deprecating style of campaign and marketing strategy is mainly adopted to excel brand endorsement and product sales value. In this paper, we propose an LSTM-based deep learning approach for detecting self-deprecating sarcasm in textual data. To the best of our knowledge, there is no prior work related to self-deprecating sarcasm detection using deep learning techniques. Starting with a filtering step to identify self-referential tweets, the proposed approach adopts a deep learning model using LSTM for detecting self-deprecating sarcasm. The proposed approach is evaluated over three Twitter datasets and performs significantly better in terms of precision, recall, and f-score.
%U https://aclanthology.org/2019.icon-1.24/
%P 201-210

Markdown (Informal)

[An LSTM-Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data](https://aclanthology.org/2019.icon-1.24/) (Kamal & Abulaish, ICON 2019)

• An LSTM-Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data (Kamal & Abulaish, ICON 2019)

ACL

• Ashraf Kamal and Muhammad Abulaish. 2019. An LSTM-Based Deep Learning Approach for Detecting Self-Deprecating Sarcasm in Textual Data. In Proceedings of the 16th International Conference on Natural Language Processing, pages 201–210, International Institute of Information Technology, Hyderabad, India. NLP Association of India.