Sarcasm detection methods based on machine learning and deep learning

Research Article
Open access

Sarcasm detection methods based on machine learning and deep learning

ZhiYu Zhang 1*
  • 1 Shanghai University of Electric Power    
  • *corresponding author 1849053168@mail.shiep.edu.cn
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/36/20230431
ACE Vol.36
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-297-8
ISBN (Online): 978-1-83558-298-5

Abstract

Sarcasm is an expression of emotion, which often expresses criticism and disgust in a positive tone, and we see it everywhere on social networking platforms. Because of this nature, sarcasm can be challenging to process in Natural Language Processing (NLP) Systems. Failing to detect sarcasm correctly can negatively affect the results of natural language processing tasks such as text correct and sentiment analysis. In recent years, there are more and more studies on sarcasm detection, and the research methods are diverse. According to the survey, the methods based on machine learning (ML) and deep learning (DL) account for the majority. Therefore, this paper mainly summarizes the literature on sarcasm detection based on ML and DL methods in recent years. This paper first introduces the application of sarcasm, then expounds the general architecture of sarcasm detection and makes a distinction between machine learning and deep learning. Then the author investigates the relevant literature and compares the results. Finally, the challenges and research directions in this field are summarized. The purpose of this paper is to facilitate the follow-up research of sarcasm detection.

Keywords:

sarcasm detection, machine learning, deep learning

Zhang,Z. (2024). Sarcasm detection methods based on machine learning and deep learning. Applied and Computational Engineering,36,119-127.
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References

[1]. M. A. Walker, J. E. F. Tree, P. Anand, R. Abbott and J. King, "A corpus for research on deliberation and debate", Proc. LREC, vol. 12, pp. 812-817, 2012.

[2]. Adarsh M J;Pushpa Ravikumar.Sarcasm detection in Text Data to bring out genuine sentiments for Sentimental Analysis[A].2019 1st International Conference on Advances in Information Technology (ICAIT)[C],2019

[3]. N.Majumder, S. Poria, H. Peng, N. Chhaya, E. Cambria and A. Gelbukh, "Sentiment and Sarcasm Classification With Multitask Learning," in IEEE Intelligent Systems, vol. 34, no. 3, pp. 38-43, 1 May-June 2019, doi: 10.1109/MIS.2019.2904691.

[4]. D. Maynard and M. Greenwood, "Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis", Language Resources and Evaluation Conference (LREC), 2014.

[5]. J. aboobaker and E. Ilavarasan, "A Survey on Sarcasm detection and challenges," 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020, pp. 1234-1240, doi: 10.1109/ICACCS48705.2020.9074163.

[6]. S. K. Bharti, R. Naidu and K. S. Babu, "Hyperbolic Feature-based Sarcasm Detection in Tweets: A Machine Learning Approach," 2017 14th IEEE India Council International Conference (INDICON), Roorkee, India, 2017, pp. 1-6, doi: 10.1109/INDICON.2017.8487712.

[7]. Neha Pawar;Sukhada Bhingarkar.Machine Learning based Sarcasm Detection on Twitter Data[A].2020 5th International Conference on Communication and Electronics Systems (ICCES)[C],2020

[8]. Vinoth, D., Prabhavathy, P. An intelligent machine learning-based sarcasm detection and classification model on social networks. J Supercomput 78, 10575–10594 (2022). http://doi.org.shiep.vpn358.com/10.1007/s11227-022-04312-x

[9]. Le Hoang Son;Kumar, A.;Sangwan, S.R.;Arora, A.;Nayyar, A.;Abdel-Basset, M..Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network[J].IEEE Access,2019,Vol.7: 23319-23328

[10]. A. Kumar, V. T. Narapareddy, V. Aditya Srikanth, A. Malapati and L. B. M. Neti, "Sarcasm Detection Using Multi-Head Attention Based Bidirectional LSTM," in IEEE Access, vol. 8, pp. 6388-6397, 2020, doi: 10.1109/ACCESS.2019.2963630.

[11]. S. Sangwan, M. S. Akhtar, P. Behera and A. Ekbal, "I didn’t mean what I wrote! Exploring Multimodality for Sarcasm Detection," 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9206905.

[12]. D. M. Ashok, A. Nidhi Ghanshyam, S. S. Salim, D. Burhanuddin Mazahir and B. S. Thakare, "Sarcasm Detection using Genetic Optimization on LSTM with CNN," 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 2020, pp. 1-4, doi: 10.1109/INCET49848.2020.9154090.

[13]. S. S. Salim, A. Nidhi Ghanshyam, D. M. Ashok, D. Burhanuddin Mazahir and B. S. Thakare, "Deep LSTM-RNN with Word Embedding for Sarcasm Detection on Twitter," 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 2020, pp. 1-4, doi: 10.1109/INCET49848.2020.9154162.

[14]. D. Sahoo, N. R. Paul, R. C. Balabantaray and A. U. Khan, "Sarcasm Detection Using Deep Learning," 2021 19th OITS International Conference on Information Technology (OCIT), Bhubaneswar, India, 2021, pp. 331-335, doi: 10.1109/OCIT53463.2021.00072.

[15]. D. M. Kumar and A. Patidar, "Sarcasm Detection Using Stacked Bi-Directional LSTM Model," 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2021, pp. 1-5, doi: 10.1109/ICAC3N53548.2021.9725488.

[16]. M. S. Razali, A. Abdul Halin, Y. -W. Chow, N. Mohd Norowi and S. Doraisamy, "Context-Driven Satire Detection With Deep Learning," in IEEE Access, vol. 10, pp. 78780-78787, 2022, doi: 10.1109/ACCESS.2022.3194119.

[17]. M. Jeyakarthic and J. Senthilkumar, "Optimal Bidirectional Long Short Term Memory based Sentiment Analysis with Sarcasm Detection and Classification on Twitter Data," 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), Mysuru, India, 2022, pp. 1-6, doi: 10.1109/MysuruCon55714.2022.9972540.

[18]. M.J. Awan, A. Yasin, H. Nobanee, A. A. Ali, S. Z. hahzad, M. Nabeel, et al., "Shahzad Fake News Data Exploration and Analytics", Electronics, vol. 10, no. 19, pp. 2326, 2021.

[19]. B. Singh and D. K. Sharma, "A Survey of Sarcasm Detection Techniques in Natural Language Processing," 2023 6th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 2023, pp. 1-6, doi: 10.1109/ISCON57294.2023.10112176.

[20]. Chatterjee, S., Bhattacharjee, S., Ghosh, K. et al. Class-biased sarcasm detection using BiLSTM variational autoencoder-based synthetic oversampling. Soft Comput 27, 5603–5620 (2023).

[21]. R. Gupta, J. Kumar, H. Agrawal and Kunal, "A Statistical Approach for Sarcasm Detection Using Twitter Data," 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2020, pp. 633-638, doi: 10.1109/ICICCS48265.2020.9120917.

Cite this article

Zhang,Z. (2024). Sarcasm detection methods based on machine learning and deep learning. Applied and Computational Engineering,36,119-127.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-297-8(Print) / 978-1-83558-298-5(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.36
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. M. A. Walker, J. E. F. Tree, P. Anand, R. Abbott and J. King, "A corpus for research on deliberation and debate", Proc. LREC, vol. 12, pp. 812-817, 2012.

[2]. Adarsh M J;Pushpa Ravikumar.Sarcasm detection in Text Data to bring out genuine sentiments for Sentimental Analysis[A].2019 1st International Conference on Advances in Information Technology (ICAIT)[C],2019

[3]. N.Majumder, S. Poria, H. Peng, N. Chhaya, E. Cambria and A. Gelbukh, "Sentiment and Sarcasm Classification With Multitask Learning," in IEEE Intelligent Systems, vol. 34, no. 3, pp. 38-43, 1 May-June 2019, doi: 10.1109/MIS.2019.2904691.

[4]. D. Maynard and M. Greenwood, "Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis", Language Resources and Evaluation Conference (LREC), 2014.

[5]. J. aboobaker and E. Ilavarasan, "A Survey on Sarcasm detection and challenges," 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2020, pp. 1234-1240, doi: 10.1109/ICACCS48705.2020.9074163.

[6]. S. K. Bharti, R. Naidu and K. S. Babu, "Hyperbolic Feature-based Sarcasm Detection in Tweets: A Machine Learning Approach," 2017 14th IEEE India Council International Conference (INDICON), Roorkee, India, 2017, pp. 1-6, doi: 10.1109/INDICON.2017.8487712.

[7]. Neha Pawar;Sukhada Bhingarkar.Machine Learning based Sarcasm Detection on Twitter Data[A].2020 5th International Conference on Communication and Electronics Systems (ICCES)[C],2020

[8]. Vinoth, D., Prabhavathy, P. An intelligent machine learning-based sarcasm detection and classification model on social networks. J Supercomput 78, 10575–10594 (2022). http://doi.org.shiep.vpn358.com/10.1007/s11227-022-04312-x

[9]. Le Hoang Son;Kumar, A.;Sangwan, S.R.;Arora, A.;Nayyar, A.;Abdel-Basset, M..Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network[J].IEEE Access,2019,Vol.7: 23319-23328

[10]. A. Kumar, V. T. Narapareddy, V. Aditya Srikanth, A. Malapati and L. B. M. Neti, "Sarcasm Detection Using Multi-Head Attention Based Bidirectional LSTM," in IEEE Access, vol. 8, pp. 6388-6397, 2020, doi: 10.1109/ACCESS.2019.2963630.

[11]. S. Sangwan, M. S. Akhtar, P. Behera and A. Ekbal, "I didn’t mean what I wrote! Exploring Multimodality for Sarcasm Detection," 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9206905.

[12]. D. M. Ashok, A. Nidhi Ghanshyam, S. S. Salim, D. Burhanuddin Mazahir and B. S. Thakare, "Sarcasm Detection using Genetic Optimization on LSTM with CNN," 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 2020, pp. 1-4, doi: 10.1109/INCET49848.2020.9154090.

[13]. S. S. Salim, A. Nidhi Ghanshyam, D. M. Ashok, D. Burhanuddin Mazahir and B. S. Thakare, "Deep LSTM-RNN with Word Embedding for Sarcasm Detection on Twitter," 2020 International Conference for Emerging Technology (INCET), Belgaum, India, 2020, pp. 1-4, doi: 10.1109/INCET49848.2020.9154162.

[14]. D. Sahoo, N. R. Paul, R. C. Balabantaray and A. U. Khan, "Sarcasm Detection Using Deep Learning," 2021 19th OITS International Conference on Information Technology (OCIT), Bhubaneswar, India, 2021, pp. 331-335, doi: 10.1109/OCIT53463.2021.00072.

[15]. D. M. Kumar and A. Patidar, "Sarcasm Detection Using Stacked Bi-Directional LSTM Model," 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2021, pp. 1-5, doi: 10.1109/ICAC3N53548.2021.9725488.

[16]. M. S. Razali, A. Abdul Halin, Y. -W. Chow, N. Mohd Norowi and S. Doraisamy, "Context-Driven Satire Detection With Deep Learning," in IEEE Access, vol. 10, pp. 78780-78787, 2022, doi: 10.1109/ACCESS.2022.3194119.

[17]. M. Jeyakarthic and J. Senthilkumar, "Optimal Bidirectional Long Short Term Memory based Sentiment Analysis with Sarcasm Detection and Classification on Twitter Data," 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), Mysuru, India, 2022, pp. 1-6, doi: 10.1109/MysuruCon55714.2022.9972540.

[18]. M.J. Awan, A. Yasin, H. Nobanee, A. A. Ali, S. Z. hahzad, M. Nabeel, et al., "Shahzad Fake News Data Exploration and Analytics", Electronics, vol. 10, no. 19, pp. 2326, 2021.

[19]. B. Singh and D. K. Sharma, "A Survey of Sarcasm Detection Techniques in Natural Language Processing," 2023 6th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 2023, pp. 1-6, doi: 10.1109/ISCON57294.2023.10112176.

[20]. Chatterjee, S., Bhattacharjee, S., Ghosh, K. et al. Class-biased sarcasm detection using BiLSTM variational autoencoder-based synthetic oversampling. Soft Comput 27, 5603–5620 (2023).

[21]. R. Gupta, J. Kumar, H. Agrawal and Kunal, "A Statistical Approach for Sarcasm Detection Using Twitter Data," 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2020, pp. 633-638, doi: 10.1109/ICICCS48265.2020.9120917.

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