A smart video analytical framework for sarcasm detection using novel adaptive fusion network and SarcasNet-99 model
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-023-03224-y.pdf
Reference25 articles.
1. Chatterjee, S., Bhattacharjee, S., Ghosh, K., Das, A.K., Banerjee, S.: Class-biased sarcasm detection using BiLSTM variational autoencoder-based synthetic oversampling. Soft. Comput. 8, 1–8 (2023). https://doi.org/10.1007/s00500-023-08045-8
2. Moores, B., Mago, V.: A survey on automated sarcasm detection on Twitter (2022). arXiv preprint https://doi.org/10.48550/arXiv.2202.02516
3. Rahma, A., Azab, S.S., Mohammed, A.: A comprehensive review on arabic sarcasm detection: approaches, challenges and future trends. IEEE Access 8, 24 (2023)
4. Bhat, A., Jha, G.N.: Sarcasm detection of textual data on online socialmedia: a review. In: 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 1981–1985. IEEE (2022). https://10.0.4.85/ICACITE53722.2022.9823869
5. Dutta, P., Bhattacharyya, C.K.: Multi-modal sarcasm detection in social networks: a comparative review. In: 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), pp. 207–214. IEEE (2022). https://10.0.4.85/ICCMC53470.2022.9753981
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