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Using LSTM for Context Based Approach of Sarcasm Detection in Twitter

Published: 03 July 2020 Publication History

Editorial Notes

A corrigendum was issued for this paper on August 25, 2020. You can download the corrigendum from the supplemental material section of this citation page.

Abstract

In this research, we propose a sarcasm detection by taking into consideration its many varying contexts, related to the word or phrase in a tweet. To get the related context, we extract the information with paragraph2vec to simplify the process of finding the contextual meaning. The result paragraph2vec will provide the features to help classification in Long Short Term Memory (LSTM). Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. We applied a sarcasm detection method to identify sarcasm in two different languages: English and Indonesian and classification with balanced and imbalanced data. It aims to measure the reliability of the proposed approach and how effective the method is in detecting sarcasm. The result of the experiment shows that in Indonesian, balanced data has a good accuracy of 88.33 % and imbalanced data of 76.66 %, whereas in English the balanced data has an accuracy of 79% and imbalanced data of 54.5%.

Supplementary Material

a19-khotijah-corrigendum (a19-khotijah-corrigendum.pdf)
Corrigendum to "Using LSTM for Context Based Approach of Sarcasm Detection in Twitter" by Khotijah et al., Proceedings of the 11th International Conference on Advances in Information Technology (IAIT '20).

References

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Erol Özkan. SARCASM DETECTION IN TWITTER. Yaaay! it's a holiday weekend and I have a sarcasm detection project to complete! couldn't be more thrilled!. Department of Computer Engineering, Hacettepe University.https://github.com/ErolOZKAN-/NaturalLanguageProcessing-SarcasmDetection
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Cited By

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  • (2024)BERT Model Adoption for Sarcasm Detection on Twitter DataVFAST Transactions on Software Engineering10.21015/vtse.v12i3.190812:3(177-198)Online publication date: 28-Sep-2024
  • (2024)Sarcasm Through the Looking Glass: Multi-Domain Analysis for Improved DetectionSoutheastCon 202410.1109/SoutheastCon52093.2024.10500167(650-654)Online publication date: 15-Mar-2024
  • (2024)Classification of Indonesian Sarcasm Tweets on X Platform Using Deep Learning2024 7th International Conference on Informatics and Computational Sciences (ICICoS)10.1109/ICICoS62600.2024.10636904(388-393)Online publication date: 17-Jul-2024
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    IAIT '20: Proceedings of the 11th International Conference on Advances in Information Technology
    July 2020
    370 pages
    ISBN:9781450377591
    DOI:10.1145/3406601
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Microsoft Corporation: Microsoft Corporation
    • NECTEC: National Electronics and Computer Technology Center
    • KMUTT: King Mongkut's University of Technology Thonburi

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    New York, NY, United States

    Publication History

    Published: 03 July 2020

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    Author Tags

    1. Sarcasm detection
    2. context
    3. deep learning
    4. lstm
    5. paragraph2vec

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    Cited By

    View all
    • (2024)BERT Model Adoption for Sarcasm Detection on Twitter DataVFAST Transactions on Software Engineering10.21015/vtse.v12i3.190812:3(177-198)Online publication date: 28-Sep-2024
    • (2024)Sarcasm Through the Looking Glass: Multi-Domain Analysis for Improved DetectionSoutheastCon 202410.1109/SoutheastCon52093.2024.10500167(650-654)Online publication date: 15-Mar-2024
    • (2024)Classification of Indonesian Sarcasm Tweets on X Platform Using Deep Learning2024 7th International Conference on Informatics and Computational Sciences (ICICoS)10.1109/ICICoS62600.2024.10636904(388-393)Online publication date: 17-Jul-2024
    • (2024)Sarcasm Detection in Indonesian-English Code-Mixed Text Using Multihead Attention-Based Convolutional and Bi-Directional GRUIEEE Access10.1109/ACCESS.2024.343610712(137063-137079)Online publication date: 2024
    • (2024)IdSarcasm: Benchmarking and Evaluating Language Models for Indonesian Sarcasm DetectionIEEE Access10.1109/ACCESS.2024.341695512(87323-87332)Online publication date: 2024
    • (2024)Liquid–Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural NetworksIndustrial & Engineering Chemistry Research10.1021/acs.iecr.4c0001463:17(7853-7875)Online publication date: 22-Apr-2024
    • (2023)Sarcasm Detection in Telugu and Tamil: An Exploration of Machine Learning and Deep Neural Networks2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10306775(1-7)Online publication date: 6-Jul-2023
    • (2023)Performance analysis of various sarcasm detection algorithms based on feature extraction methodsINTERNATIONAL CONFERENCE ON HUMANS AND TECHNOLOGY: A HOLISTIC AND SYMBIOTIC APPROACH TO SUSTAINABLE DEVELOPMENT: ICHT 202210.1063/5.0138753(020008)Online publication date: 2023
    • (2023)Class-biased sarcasm detection using BiLSTM variational autoencoder-based synthetic oversamplingSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-07956-w27:9(5603-5620)Online publication date: 8-Mar-2023
    • (2022)Comprehensive Sarcasm Detection using Classification Models and Neural Networks2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS54159.2022.9785305(1309-1314)Online publication date: 25-Mar-2022
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