Abstract
Sarcasm has become quite inter-related with the day to day life of all. In news robust sarcasm is often used to grab the attention of the viewers. This research aims to detect sarcasm using Evidential deep learning. This technique uses uncertainty estimations for identifying the sentiments from news headlines dataset. Also, LSTM and GRU have been used with Evidential deep learning approach. The purpose of using LSTM is that it can classify texts from headlines in order to analysis the sentiments. Moreover, we have used GRU which is an recurrent neural networks (RNN) and it effectively models sequential data. The architecture of the GRU network is ideally suited for identifying dependencies and extended contextual relationships within news headings. Overall, our proposed model uses Evidential deep learning based LSTM and GRU to identify the sentiments of robust sarcasms from news headlines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chaudhari, P., Chandankhede, C.: Literature survey of sarcasm detection. In: 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 2041–2046 (2017)
Krishnan, N., Rethnaraj, J., Saravanan, M.: Sentiment topic sarcasm mixture model to distinguish sarcasm prevalent topics based on the sentiment bearing words in the tweets. J. Amb. Intell. Human. Comput. 12, 6801–6810 (2021)
Goel, P., Jain, R., Nayyar, A., Singhal, S., Srivastava, M.: Sarcasm detection using deep learning and ensemble learning. Multimed. Tools App. 81, 43229–43252 (2022)
Jena, A., Sinha, A., Agarwal, R.: C-Net: contextual network for sarcasm detection (2020)
Potamias, R., Siolas, G., Stafylopatis, A.: A Transformer-based approach to Irony and Sarcasm detection (2019)
Rajadesingan, A., Zafarani, R., Liu, H.: Sarcasm detection on twitter: a behavioral modeling approach. In: WSDM 2015 - Proceedings Of The 8th ACM International Conference on Web Search and Data Mining, pp. 97–106 (2015)
Bouazizi, M., Ohtsuki, T.: A pattern-based approach for sarcasm detection on twitter. IEEE Access 4, 5477–5488 (2016)
Ptácek, T., Habernal, I., Hong, J.: Sarcasm Detection on Czech and English Twitter. In: International Conference on Computational Linguistics (2014)
Sarsam, S., Al-Samarraie, H., Alzahrani, A., Wright, B.: Sarcasm detection using machine learning algorithms in Twitter: a systematic review. Int. J. Mark. Res. 62, 578–598 (2020)
Sharma, D., Singh, B., Agarwal, S., Pachauri, N., Alhussan, A., Abdallah, H.: Sarcasm detection over social media platforms using hybrid ensemble model with fuzzy logic. Electronics 12, 937 (2023)
Islam, M., et al.: RNN variants vs transformer variants: uncertainty in text classification with monte Carlo dropout. In: 2022 25th International Conference On Computer And Information Technology (ICCIT), pp. 7–12 (2022)
Wang, C., et al.: Uncertainty estimation for stereo matching based on evidential deep learning. Pattern Recogn. 124, 108498 (2022)
Bao, W., Yu, Q., Kong, Y.: Evidential deep learning for open set action recognition. In: Proceedings Of The IEEE/CVF International Conference on Computer Vision (ICCV), pp. 13349–13358 (2021)
Capellier, E., Davoine, F., Cherfaoui, V., Li, Y.: Evidential deep learning for arbitrary LIDAR object classification in the context of autonomous driving. In: 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 1304–1311 (2019)
Ulmer, D., Hardmeier, C., Frellsen, J.: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation, Prior and Posterior Networks (2023)
Misra, R., Arora, P.: Sarcasm detection using news headlines dataset. AI Open. 4, 13–18 (2023)
Pietrantuono, R., Popov, P., Russo, S.: Reliability assessment of service-based software under operational profile uncertainty. Reliab. Eng. Syst. Saf. 204, 107193 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chy, M.S.R., Chy, M.S.R., Mahin, M.R.H., Rahman, M.M., Hossain, M.S., Rasel, A.A. (2023). Sarcasm Detection in News Headlines Using Evidential Deep Learning-Based LSTM and GRU. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14406. Springer, Cham. https://doi.org/10.1007/978-3-031-47634-1_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-47634-1_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47633-4
Online ISBN: 978-3-031-47634-1
eBook Packages: Computer ScienceComputer Science (R0)