Identifying sarcasm using heterogeneous word embeddings: a hybrid and ensemble perspective
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00500-023-08368-6.pdf
Reference46 articles.
1. Ahuja R, Sharma SC (2022) Transformer-based word embedding with cnn model to detect sarcasm and irony. Arab J Sci Eng 47(8):9379–9392. https://doi.org/10.1007/s13369-021-06193-3
2. Akula R, Garibay I (2021) Interpretable multi-head self-attention architecture for sarcasm detection in social media. Entropy 23(4):394. https://doi.org/10.3390/e23040394
3. Albahar M (2021) A hybrid model for fake news detection: Leveraging news content and user comments in fake news. IET Inform Secur 15(2):169–177. https://doi.org/10.1049/ise2.12021
4. Ay Karakuş B, Talo M, Hallaç İR et al (2018) Evaluating deep learning models for sentiment classification. Concurr Comput Pract Exp 30(21):e4783. https://doi.org/10.1002/cpe.4783
5. Azwar AS, et al (2020) Sarcasm detection using multi-channel attention based blstm on news headline https://doi.org/10.21203/rs.3.rs-63423/v1
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