How Latest Computer Science Research Copes with COVID-19?

Author:

Bayoudhi LeilaORCID,Sassi Najla,Jaziri Wassim

Publisher

Springer International Publishing

Reference37 articles.

1. Basiri, M.E., Nemati, S., Abdar, M., Asadi, S., Acharrya, U.R.: A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowl. Based Syst. 228, 107242 (2021)

2. dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. pp. 69–78. Dublin City University and Association for Computational Linguistics, Dublin, Ireland (2014)

3. Jabreel, M., Hassan, F., Moreno, A.: Target-dependent sentiment analysis of tweets using bidirectional gated recurrent neural networks. In: Hatzilygeroudis, I., Palade, V. (eds.) Advances in Hybridization of Intelligent Methods: Models, Systems and Applications, pp. 39–55. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-66790-4

4. Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of Tricks for Efficient Text Classification. arXiv:1607.01759 [cs] (2016)

5. Sanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv:1910.01108 [cs] (2020)

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